RESEARCH CENTRES

Inria Lyon Centre

Inria Centre
at Université Grenoble Alpes

IN PARTNERSHIP WITH:

Institut national des sciences appliquées
de Lyon

2023
ACTIVITY REPORT

Project-Team

CHROMA

Cooperative and Human-aware Robot
Navigation in Dynamic Environments

IN COLLABORATION WITH: Centre of Innovation in Telecommunications
and Integration of services

DOMAIN

Perception, Cognition and Interaction

THEME

Robotics and Smart environments

Contents

Project-Team CHROMA

1 Team members, visitors, external collaborators

2 Overall objectives

2.1 Origin of the project
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 Research themes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3 Research program

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 Perception and Situation Awareness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3 Decision Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1

3

5
5
5

6
6
7
9

4 Application domains

12
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
4.2 Future cars and transportation systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4.3 Service robotics with ground and aerial robots . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

5 Social and environmental responsibility

14
5.1 Footprint of research activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
5.2 Impact of research results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

6 Highlights of the year

14
6.1 Awards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
6.2 Team life . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
6.3 New projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

7 New software, platforms, open data

15
7.1 New software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
7.1.1 CMCDOT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
7.1.2 Ground Elevation and Occupancy Grid Estimator (GEOG - Estimator) . . . . . . . . . 15
7.1.3 Zoe Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
7.1.4 Hybrid-state E* . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
7.1.5 Pedsim_ros_AV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
7.1.6 S-NAMO-SIM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
7.1.7 SimuDronesGR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
7.1.8 spank . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
7.2 New platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
7.2.1 Chroma Aerial Robots platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
7.2.2 Barakuda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

8 New results

19
8.1 Bayesian Perception . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
8.1.1 Evolution of the CMCDOT framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
8.1.2 Cooperative Perception through communication with Dynamic Occupancy Grids . 19
8.1.3 Generation of proper probabilistic occupancy grids by neural network . . . . . . . . 19
8.1.4 From Probabilistic Occupancy Grids to versatile Collision Avoidance using Predictive

Collision Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
8.1.5 Validation of AI-based algorithms in autonomous vehicles
. . . . . . . . . . . . . . . 20
8.1.6 Navigation-Based Evaluation Metric for Probabilistic Occupancy Grids . . . . . . . . 21
8.2 Situation Awareness & Decision-making for Autonomous Vehicles . . . . . . . . . . . . . . . 21
8.2.1 Situation Understanding and Motion Forecasting . . . . . . . . . . . . . . . . . . . . . 21
8.2.2 Semantic Grid Generation from Camera and LiDAR Sensor Fusion . . . . . . . . . . . 22
8.2.3 Lidar-RGB Sensor Fusion with Transformers for Semantic Grid Prediction . . . . . . 22
. . 23
8.2.4 Dynamic Scene Prediction for Urban Traffic Scene Using Occupancy Grid Maps

8.2.5 Temporal Multi-Modality Fusion Towards Occlusion-Aware Semantic Segmentation 23
8.3 Robust state estimation (Sensor fusion) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
8.4 Online planning and learning for robot navigation . . . . . . . . . . . . . . . . . . . . . . . . 25
8.4.1 Motion-planning in dense pedestrian environments . . . . . . . . . . . . . . . . . . . 25
8.4.2 Attention Graph for Multi-Robot Social Navigation with Deep Reinforcement Learning 25
8.4.3 Deep-Learning for vision-based navigation . . . . . . . . . . . . . . . . . . . . . . . . 26
8.4.4 Navigation Among Movable Obstacles: extension of NAMO to multi-robot and social

constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
8.5 Swarm of UAVs: decentralized control, path-planning and communication . . . . . . . . . 28
8.5.1 Multi-UAV Exploration and Visual Coverage of 3D Environments . . . . . . . . . . . . 28
8.5.2 Decentralized control of swarm of UAVs: Flocking models . . . . . . . . . . . . . . . . 28
. . . . . . . . . . . . . . . . . . . 29
8.5.3 Communication and localization in swarm of UAVs
8.6 Multi-Agent Sequential Decision-Making under Uncertainty . . . . . . . . . . . . . . . . . . 30
8.6.1 Global Min-Max Computation for alpha-Hölder Zero-Sum Games . . . . . . . . . . . 30
8.6.2 Optimally Solving Decentralized POMDPs Under Hierarchical Information Sharing
31
8.6.3 Optimally Solving Decentralized POMDPs: The Sequential Central Learning Approach 31
8.6.4 Learning To Act Optimally in Sequential-Move Dec-POMDPs: Quantized Q-learning

8.7 Constrained Optimisation problems

Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
8.6.5 Stable Reinforcement Learning Methods . . . . . . . . . . . . . . . . . . . . . . . . . . 32
. . . . . . . . . . . . . . . . . . . 32
8.6.6 Event-based Neural Learning for Quadrotor Control
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
8.7.1 Constraint Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
8.7.2 Multi-Robot Path Planning with cable constraints . . . . . . . . . . . . . . . . . . . . . 33
8.7.3 Vehicle routing problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

9 Bilateral contracts and grants with industry

34
9.1 Bilateral contracts with industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
9.1.1 Toyota Motor Europe (2006 - 2023) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
9.1.2 Sumitomo Electric Industries (2020-23) . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
9.2 Bilateral grants with industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
. . . . 34
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

9.2.1 IRT Nanoelec PULSE – Security of Autonomous Mobile Robotics (2023-2025)
9.2.2 MeanWhile (2021 - 2023)

10 Partnerships and cooperations

10.2.1 H2020 projects

10.2 European initiatives

10.1 International research visitors

35
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
10.1.1 Visits of international scientists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
10.3 National initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
10.3.1 ANR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
10.3.2 PEPR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
10.3.3 Grand Defis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
10.3.4 DGA/INRIA AI projects
10.4 Regional initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

11 Dissemination

40
11.1 Promoting scientific activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
11.1.1 Scientific events: organisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
11.1.2 Scientific events: selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
11.1.3 Journal
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
11.1.4 Leadership within the scientific community . . . . . . . . . . . . . . . . . . . . . . . . 42
11.1.5 Scientific expertise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
11.1.6 Research administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
11.2 Teaching - Supervision - Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
11.2.1 Supervision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

11.2.2 Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
11.2.3 Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
11.2.4 Interventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

12 Scientific production

45
12.1 Major publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
12.2 Publications of the year
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
12.3 Other . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
12.4 Cited publications

Project CHROMA

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Project-Team CHROMA

Creation of the Project-Team: 2017 December 01

Keywords

Computer sciences and digital sciences

A1.3.2. – Mobile distributed systems

A1.5.2. – Communicating systems

A2.3.1. – Embedded systems

A3.4.1. – Supervised learning

A3.4.2. – Unsupervised learning

A3.4.3. – Reinforcement learning

A3.4.4. – Optimization and learning

A3.4.5. – Bayesian methods

A3.4.6. – Neural networks

A3.4.8. – Deep learning

A5.1. – Human-Computer Interaction

A5.4.1. – Object recognition

A5.4.2. – Activity recognition

A5.4.4. – 3D and spatio-temporal reconstruction

A5.4.5. – Object tracking and motion analysis

A5.4.6. – Object localization

A5.4.7. – Visual servoing

A5.10.2. – Perception

A5.10.3. – Planning

A5.10.4. – Robot control

A5.10.5. – Robot interaction (with the environment, humans, other robots)

A5.10.6. – Swarm robotics

A5.10.7. – Learning

A5.11.1. – Human activity analysis and recognition

A6.1.2. – Stochastic Modeling

A6.1.3. – Discrete Modeling (multi-agent, people centered)

A6.2.3. – Probabilistic methods

A6.2.6. – Optimization

A6.4.1. – Deterministic control

A6.4.2. – Stochastic control

A6.4.3. – Observability and Controlability

A8.2. – Optimization

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Inria Annual Report 2023

A8.2.1. – Operations research

A8.2.2. – Evolutionary algorithms

A8.11. – Game Theory

A9.2. – Machine learning

A9.5. – Robotics

A9.6. – Decision support

A9.7. – AI algorithmics

A9.9. – Distributed AI, Multi-agent

A9.10. – Hybrid approaches for AI

Other research topics and application domains

B5.2.1. – Road vehicles

B5.6. – Robotic systems

B7.1.2. – Road traffic

B8.4. – Security and personal assistance

Project CHROMA

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1 Team members, visitors, external collaborators

Research Scientists

• Christian Laugier [INRIA, Emeritus, HDR]

• Agostino Martinelli [INRIA, Researcher]

• Alessandro Renzaglia [INRIA, Researcher]

• David Sierra Gonzalez [INRIA, Starting Research Position, until May 2023]

Faculty Members

• Olivier Simonin [Team leader, INSA LYON, Professor, HDR]

• Jilles Dibangoye [INSA LYON, Associate Professor, until Jun 2023, HDR]

• Christine Solnon [INSA LYON, Professor, HDR]

• Anne Spalanzani [UGA, Professor, (convention with Inria), HDR]

Post-Doctoral Fellows

• Jacopo Castellini [INSA LYON, Post-Doctoral Fellow, from Jul 2023]

• Wenqian Liu [INRIA, Post-Doctoral Fellow, until Sep 2023]

• Johan Peralez [INSA LYON, Post-Doctoral Fellow]

• Oscar Tellez Sanchez [INSA LYON, Post-Doctoral Fellow, until Jun 2023]

PhD Students

• Rabbia Asghar [INRIA]

• Théotime Balaguer [INSA LYON]

• Kaushik Bhowmik [INRIA, from May 2023]

• Alexandre Bonnefond [POLE EMPLOI, from Apr 2023 until Jun 2023]

• Alexandre Bonnefond [INRIA, until Mar 2023]

• Aurélien Delage [INSA LYON, ATER, from Sep 2023]

• Aurélien Delage [INSA LYON, until Aug 2023]

• Manuel Diaz Zapata [INRIA]

• Romain Fontaine [INSA LYON, until Sep 2023]

• Thomas Genevois [POLE EMPLOI, from Jul 2023]

• Jean-Baptiste Horel [INRIA]

• Pierre Marza [INSA LYON]

• Xiao Peng [INSAVALOR]

• Benoit Renault [POLE EMPLOI]

• Benjamin Sportich [INRIA, from Nov 2023]

• Samuele Zoboli [INSA LYON, until Sep 2023]

Inria Annual Report 2023

4

Technical Staff

• Thierry Arrabal [INSA LYON, Engineer]

• Hugo Bantignies [INRIA, Engineer, from Apr 2023]

• Robin Baruffa [INRIA, Engineer]

• David Brown [INRIA, Engineer, from Nov 2023]

• Johan Faure [INRIA, Engineer]

• Simon Ferrier [INSAVALOR, Engineer]

• Romain Fontaine [INSAVALOR, Engineer, from Oct 2023]

• Thomas Genevois [INRIA, Engineer, until Jun 2023]

• Pierrick Koch [INRIA, Engineer, until Feb 2023]

• Wenqian Liu [INRIA, Engineer, from Oct 2023]

• Khushdeep Singh Mann [INRIA, Engineer, from May 2023]

• Khushdeep Singh Mann [INRIA, Engineer, until Feb 2023]

• Damien Rambeau [INSAVALOR, Engineer, from Sep 2023]

• Lukas Rummelhard [INRIA, Engineer, IR SED Grenoble, 100% Chroma]

• Gustavo Andres Salazar Gomez [INRIA, Engineer]

• Abhishek Tomy [INRIA, Engineer, until Feb 2023]

• Nicolas Valle [INRIA, Engineer, from Nov 2023, IR SED Lyon, 50% Chroma]

• Stéphane d’Alu [INSA LYON, Engineer, IR CITI, INSA Lyon, 40% Chroma]

Interns and Apprentices

• Marco Ambrogi [INSAVALOR, Intern, until Mar 2023]

• Brandon Da Silva Alves [INSAVALOR, Intern, until Jun 2023]

• Erwan Escudie [INSA LYON, Intern, from Feb 2023 until Jul 2023]

Administrative Assistant

• Anouchka Ronceray [INRIA]

Visiting Scientist

• Marco Legittimo [ERASMUS+, from Jun 2023 until Oct 2023]

External Collaborators

• Jilles Dibangoye [UNIV GRONINGEN, from Jul 2023, HDR]

• Fabrice Jumel [CPE LYON, CITI lab.]

• Jacques Saraydaryan [CPE LYON, CITI lab.]

Project CHROMA

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2 Overall objectives

2.1 Origin of the project

Chroma is a bi-localized project-team at Inria Lyon and Inria Grenoble (in Auvergne-Rhône-Alpes region).
The project was launched in 2015 before it became officially an Inria project-team on December 1st,
2017. It brings together experts in perception and decision-making for mobile robotics and intelligent
transport, all of them sharing common approaches that mainly relate to the field of Artificial Intelligence.
It was originally founded by members of the working group on robotics at CITI lab1, led by Prof. Olivier
Simonin (INSA Lyon2), and members from Inria project-team eMotion (2002-2014), led by Christian
Laugier, at Inria Grenoble. Earlier members include Olivier Simonin (Prof. INSA Lyon), Christian Laugier
(Inria researcher DR, Grenoble), Anne Spalanzani (Prof., UGA), Jilles Dibangoye (Asso. Prof. INSA Lyon)
and Agostino Martinelli (Inria researcher CR, Grenoble). On January 2020, Christine Solnon (Prof. INSA
Lyon) joined the team, thanks to her transfer from LIRIS lab. to CITI lab. On October 2021, Alessandro
Renzaglia (Inria researcher CR, Lyon) was recruited through the Inria researcher recruitment campaign.
The overall objective of Chroma is to address fundamental and open issues that lie at the intersection
of the emerging research fields called “Human Centered Robotics” 3, “Multi-Robot Systems" 4, and AI for
humanity.

More precisely, our goal is to design algorithms and models that allow autonomous agents to
perceive, decide, learn, and finally adapt to their environment. A focus is given to unknown and
human-populated environments, where robots or vehicles have to navigate and cooperate to fulfill
complex tasks.

In this context, recent advances in embedded computational power, sensor and communication
technologies, and miniaturized mechatronic systems, make the required technological breakthroughs
possible.

Chroma is clearly positioned in the "Artificial Intelligence and Autonomous systems" research theme
of the Inria 2018-2022 Strategic Plan. More specifically we refer to the "Augmented Intelligence" chal-
lenge (connected autonomous vehicles) and to the "Human centred digital world" challenge (interactive
adaptation).

2.2 Research themes

To address the mentioned challenges, we take advantage of recent advances in: probabilistic methods,
machine learning, planning techniques, multi-agent decision making, and constrained optimisation
tools. We also draw inspiration from other disciplines such as Sociology, to take into account human
models, or Physics/Biology, to study self-organized systems.

Chroma research is organized in two main axes : i) Perception and Situation Awareness ii) Decision

Making. Next, we elaborate more about these axes.

• Perception and Situation Awareness. This theme aims at understanding complex dynamic scenes,
involving mobile objects and human beings, by exploiting prior knowledge and streams of percep-
tual data coming from various sensors. To this end, we investigate three complementary research
problems:

– Bayesian & AI based Perception: How to interpret in real-time a complex dynamic scene
perceived using a set of different sensors, and how to predict the near future evolution of this
dynamic scene and the related collision risks ? How to extract the semantic information and
to process it for the autonomous navigation step.

– Modeling and simulation of dynamic environments: How to model or learn the behavior of
dynamic agents (pedestrians, cars, cyclists...) in order to better anticipate their trajectories?

1Centre of Innovation in Telecommunications and Integration of Service, see www.citi-lab.fr
2National Institute of Applied Sciences. INSA Lyon is part of Lyon Universities
3Montreuil, V.; Clodic, A.; Ransan, M.; Alami, R., "Planning human centered robot activities," in Systems, Man and Cybernetics,

2007. ISIC. IEEE International Conference on , vol., no., pp.2618-2623, 7-10 Oct. 2007

4IEEE RAS Multi-Robot Systems multirobotsystems.org/

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Inria Annual Report 2023

– Robust state estimation: Acquire a deep understanding on several sensor fusion problems

and investigate their observability properties in the case of unknown inputs.

• Decision making. This theme aims to design algorithms and architectures that can achieve both
scalability and quality for decision making in intelligent robotic systems and more generally for
problem solving. Our methodology builds upon advantages of three (complementary) approaches:
online planning, machine learning, and NP-hard optimization problem solving.

– Online planning: In this theme we study planning algorithms for single and fleet of cooper-
ative mobile robots when they face complex and dynamics environments, i.e. populated by
humans and/or mostly unknown.

– Machine learning: We search for structural properties–e.g., uniform continuity–which enable
us to design efficient planning and (deep) reinforcement learning methods to solving complex
single or multi-agent decision-making tasks.

– Offline constrained optimisation problem: We design and study approaches based on Con-
straint Programming (CP) and meta-heuristics to solve NP-hard problems such as planning
and routing problems, for example.

Chroma is also concerned with applications and transfer of the scientific results. Our main ap-
plications include autonomous and connected vehicles, service robotics, exploration & mapping tasks
with ground and aerial robots. Chroma is currently involved in several projects in collaboration with
automobile companies (Renault, Toyota) and some startups (see Section 4).

The team has its own robotic platforms to support the experimentation activity (see5). In Grenoble,
we have two experimental vehicles equipped with various sensors: a Toyota Lexus and a Renault Zoe;
the Zoe car has been automated in December 2016. We have also developed two experimental test
tracks respectively at Inria Grenoble (including connected roadside sensors and a controlled dummy
pedestrian) and at IRT Nanoelec & CEA Grenoble (including a road intersection with traffic lights and
several urban road equipments). In Lyon, we have a fleet of UAVs (Unmanned Aerial Vehicles) composed
of 4 PX4 Vision, 2 IntelAero and 5 mini-UAVs Crazyflies. We have also a fleet of ground robots composed
of 16 Turtlebot and 3 humanoids Pepper. The platforms are maintained and developed by contractual
engineers, and by Lukas Rummelhard, from SED, in Grenoble.

3 Research program

3.1 Introduction

The Chroma team aims to deal with different issues of autonomous navigation and intelligent transport:
perception, decision-making and cooperation. Figure 1 schemes the different themes and sub-themes
investigated by Chroma.

We present here after our approaches to address these different themes of research, and how they
combine altogether to contribute to the general problem of robot navigation. Chroma pays particular at-
tention to the problem of autonomous navigation in highly dynamic environments populated by humans
and cooperation in multi-robot systems. We share this goal with other major robotic laboratories/teams
in the world, such as Autonomous Systems Lab at ETH Zurich, Robotic Embedded Systems Laboratory
at USC, KIT 6 (Prof. Christoph Stiller lab and Prof. Ruediger Dillmann lab), UC Berkeley, Vislab Parma
(Prof. Alberto Broggi), and iCeiRA 7 laboratory in Taipei, to cite a few. Chroma collaborates at various
levels (visits, postdocs, research projects, common publications, etc.) with most of these laboratories.

5team.inria.fr/chroma/en/plate-formes/
6Karlsruhe Institut fur Technologie
7International Center of Excellence in Intelligent Robotics and Automation Research.

Project CHROMA

7

Figure 1: Research themes of the team and their relation

3.2 Perception and Situation Awareness

Project-team positioning

The team carries out research across the full (software) stack of an autonomous driving system. This goes
from perception of the environment to motion forecasting and trajectory optimization. While most of the
techniques that we develop can stand on their own and are typically tested on our experimental platforms,
we slowly move towards their complete integration. This will result in a functioning autonomous driving
software stack. Beyond that point, we envision to focus our research on developing new methods that
improve the individual performance of each of the stack’s components, as well as the global performance.
This is alike to how autonomous driving companies operate (e.g. Waymo, Cruise, Tesla). The research
topics that we explore are addressed by a very long list of international research laboratories and private
companies. However, in terms of scope, we find the research groups of Prof. Dr.-Ing. Christoph Stiller
(KIT), Prof. Marcelo Ang (NUS), Prof. Daniela Rus (MIT) and Prof. Wolfram Burgard (TRI California) to
be the closest to us; we have active interconnections with these research groups (e.g. in the scope of
the IEEE RAS Technical Committee AGV-ITS we are co-chairing any of the related annual Workshops).
At the national level, we are cooperating in the framework of several R&D projects with UTC, Institut
Pascal Clermont-Ferrand, University Gustave Eiffel, and the Inria Teams ASTRA, Acentauri and Convecs.
Our main research originality relies in the combination of Bayesian approaches, AI technologies, Human-
Vehicle interactions models, Robust State Estimation formalism, and a mix of Real experiments and Formal
methods for validating our technologies in complex real world dynamic scenarios.

Our research on visual-inertial sensor fusion is closely related to the research carried out by the
group of Prof. Stergios Roumeliotis (University of Minnesota) and the group of Prof. Davide Scaramuzza
(University of Zurich). Our originality in this context is the introduction of closed-form solutions instead
of filter-based approaches.

Scientific achievements

1. Bayesian & AI based Environment Perception and Forecasting

In order to be able to operate in a dynamic environment surrounded by humans or other robots,
an autonomous system needs to Perceive, Understand and Forecast any changes of its surroundings.
We have investigated the perception task from different angles, most of them with the shared nexus of
involving both Bayesian approaches and Deep Learning models.

Bayesian Perception: The team’s work on Bayesian approaches has given rise over the last decade
to numerous publications and to some patents and licensed software8. Over the past 4 years, we have
focused on collision risk assessment, safe navigation and semantic processing issues, with a strong
emphasis on the integration and the validation of our technologies on real-time experimental platforms.

8See www.mdpi.com/1424-8220/17/2/344 for a review of Bayesian Occupancy Filter approaches, and [88] for an overview of the

Chroma team activities in this field

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Inria Annual Report 2023

The new results gave rise to the filing of two patents and to a transfer of technology with a French SME
and with a Japanese international company (R&D work still ongoing). The main advances relate to
(1) the generalization of the principle of Bayesian fusion in order to be able to integrate connected
sensors located outside the ego-vehicle (infrastructure or other vehicles), (2) new real-time methods
allowing to better estimate the risks of collision and make navigation safer (including path planning),
and (3) experimental validation principle combining real tests, synchronized simulated environments
(augmented reality principle) and formal methods [55, 94, 101, 54]. A new PhD thesis addressing the
subject of validating AI-based perception components in autonomous vehicles, with a particular focus
on formal validation methods, has started in 2021 in cooperation with the Inria Convecs team (funding:
French project PRISSMA).

Situation awareness & Environment Forecasting: In addition to the above-mentioned work, we have
developed AI-based methods for extracting the semantic information of observed scenes [90, 87, 91,
120]. In particular, we have developed hybrid methods for 3D object detection [103, 124][104], semantic
mapping of the environment from a top-view [73, 74, 75] and ground plane estimation and segmentation
[102]. We have likewise investigated the use of domain adaptation techniques to mitigate the degradation
of some of the previous algorithms across different weather conditions [71, 68] and sensor modalities
[120]. In terms of environment forecasting, two PhD theses were dedicated to the task of predicting the
future behavior of surrounding traffic [53, 121]. The first of them targeted road intersections; the second
focused on highway scenarios and leveraged a behavioral model learned from driving demonstrations
[122] to cope with the long-term prediction horizon necessary for highways. Both theses also explored
how to integrate the forecasting models into a behavioral planning framework [123]. In addition to these
forecasting models, we have also carried out research on localization [51], tracking [72], detection of
abnormal behaviors [129], and contextualization of complex risk mitigation scenarios [119]. A new PhD
thesis addressing the subject of “Hybrid Sensor fusion” was launched in 2021 (funding: EU CPS4EU).

2. Modeling and simulation of dynamic environments

Modeling pedestrian’s behaviors and recognizing intentions When Autonomous vehicles and social
robots evolve in shared space where other cars, humans, cyclists, electrical scooters are present, they
must recognize their intention in term of trajectory and collaboration. In the scope of several PhD,
we proposed to model and predict various kinds of agents in the scene (P. Vasishta’s Phd (2019), M.
Kabtoul’s (2021), K. Bhowmik’s PhD started in may 2023) using maching learning (HMM, multi-criteria
optimisation or Deeplearning models). Pedestrians simulation. We proposed the simulator SPACiSS,
"Simulator for Pedestrians and an Autonomous Car in Shared Spaces", an open source simulator based
on the PedSim framework that can simulate interactions between pedestrians and vehicles in different
shared space scenarios. With the integration in the ROS framework, commonly used in robotics, the
software is designed as an environment to test autonomous navigation systems [107, 108][109, 110].

3. Robust State Estimation

Our investigations have been in the framework of the unknown input observability problem. This
problem was introduced and firstly investigated during the sixties and has remained unsolved for 50
years. During the last 8 years, we have studied this fundamental open problem. Due to its complexity,
the general solution was published in a book [98]. The main result of this year is the extension of the
above solution. Specifically, in [98], we provided the solution by restricting our investigation to systems
that satisfy a special assumption that is called canonicity with respect to the unknown inputs. In 2022,
after an exhaustive characterization of the concept of canonicity, we also accounted for the case when
this assumption is not satisfied and we obtained the most general solution of the problem. These results
have been published by the Journal of Information Fusion [97]. A further result obtained in 2022, is the
extension of the well known observability rank condition to time-varying systems. This extension has
been published by the Transaction on Automatic Control [96].

Collaborations

• Main long-term industrial collaborations: IRT Nanoelec & CEA ∼10 years), Toyota Motor Europe
(∼12 years, R&D center Zaventen-Brussels), Renault (∼10 years), SME Easymile (∼3 years), Sum-
itomo Japan (started in 2019), Iveco (3 years in the framework of STAR project). These collaborations

Project CHROMA

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are funded either by Industry or by National, Regional or European projects. The main outputs
are common publications, softwares development, patents or technological transfers. Two former
doctoral students of our team were recruited some years ago by respectively TME Zaventen and
Renault R&D centers.

• Main academic collaborations: Institut Pascal Clermont-Ferrand, University Compiegne (UTC),
University Gustave Eiffel, several Inria Teams (ASTRA, Acentauri, Convecs, Rainbow). These collab-
orations are most of the time conducted in the scope of various National R&D projects (FUI, PIA,
ANR, etc.). The main outputs are scientific and software exchanges. In addition, we had a fruitful
collaboration with B. Mourrain from the Inria-Aromath team. The goal was to find the analytical
solution of a polynomial equation system that fully characterizes the cooperative visual-inertial
sensor fusion problem with 2 agents.

• International cooperation for scientific exchanges and IEEE scientific events organization, e.g. NUS
& NTU Singapore, Peking University, TRI Mountain View, KIT Karlsruhe, Coimbra University. We
have constantly interacted with Prof. D. Scaramuzza from the university of Zurich to make our
findings in visual inertial sensor fusion more usable in a realistic context (e.g., for the autonomous
navigation of drones).

3.3 Decision Making

Project-team positioning

In his reference book Planning algorithms[93], S. LaValle discusses the different dimensions that make
the motion-planning a complex problem, which are the number of robots, the obstacle regions, the
uncertainty of perception and action, and the allowable velocities. In particular, it is emphasized that
multiple robot planning in complex environments are NP-hard problems which implies that exact
approaches have exponential time complexities in the worst case (unless P=NP). Moreover, dynamic and
uncertain environments, as human-populated ones, expand this complexity. In this context, we aim at
scaling up decision-making in human-populated environments and in multi-robot systems, while dealing
with the intrinsic limits of the robots and machines (computation capacity, limited communication).
To address these challenges, we explore new algorithms and AI architectures following three directions:
online (or real-time) planning, machine learning to adapt to the complexity of uncertain environments,
and combinatorial optimization to deal with offline constrained problems. Combining these approaches,
seeking to scale up them, and also evaluating them with real platforms are also elements of originality of
our work.

We share these goals with other laboratories/teams in the world, such as the Mobile Robotics Labor-
atory and Intelligent Systems "IntRoLab" at Sherbrooke University (Montreal) led by Prof. F. Michaud,
the Learning Agents Research Group (LARG) within the AI Lab at the University of Texas (Austin) led by
Prof. P. Stone, the Autonomous Systems Lab at ETH Zurich (Switzerland) led by R. Siegwart, the Robotic
Sensor Networks Lab at the University of Minnesota (USA) led by Prof. V. Isler, and the Autonomous
Robots Lab at NTNU (Norway) to cite a few. At Inria, we share some of the objectives with the LARSEN
team in Nancy (multi-agent machine learning, multi-robot planning) and the RAINBOW team in Rennes
(multi-UAV planning). We have also collaborations with the ACENTAURY team, in Sophia Antipolis, about
autonomous navigation among humans. In France, among other labs involved on similar subjects, we
can cite the LAAS (CNRS, Toulouse), the ISIR lab (CNRS, Paris Sorbonne Univ.) and the MAD team in
Caen University. In the more generic domain of problem solving, we can mention the TASC team at
LS2N (Nantes), the CRIL lab (Lens) and, at the international level, we can cite the Insight Centre for Data
Analytics (Cork, Ireland).

Scientific achievements

1. Online Planning

In this theme, we address online planning when robots/vehicles/UAVs have to navigate or work in
complex and generally unknown environments. The challenge comes from the necessity for entities to

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Inria Annual Report 2023

decide actions in real time and with limited information. Hereafter we present our main results following
different problem contexts.

Social and ethic navigation
We investigated new methods to build safe and socially compliant trajectories among humans using
models of behaviors presented in Axis I. A focus on navigation of an autonomous vehicle among crowds
has been done were there is no free space to plan a safe trajectory (M. Kabtoul’s PhD, ANR Hianic Project).
We proposed a solution that is a proactive social navigation framework. The system is based on the idea
of the coupled navigation behavior between the pedestrian and the vehicle in shared spaces. The system
takes into account the cooperative nature of human behavior and exploits it to explore new navigation
options, and navigate the shared space “proactively" [83]. We obtained promising results using the
SPACISS simulator previously described. Walese demonstrate how this vehicle can navigate crowds with
a limited pedestrian disturbance [85]. Some experiments have been done using the real autonomous
Zoe car and simulated crowds perceived by the car using augmented reality (T. Genevois’s PhD). We also
investigated ethic navigation when an autonomous vehicle cannot avoid a collision with an object or a
vulnerable road user (L. Serafim Guardini’s PhD). We exploit accidentology data where each class of object
or agent presents an injury probability with respect to the impact speed and ethical/economical/political
factors. Our method generates a cost map containing a collision probability along with its associated risk
of injury, which are used to plan trajectories with the lowest risk [119].

We also investigated another navigation problem, which is NAMO (Navigation Among Movable
Obstacles). We extended NAMO in two directions considering social constraints when robots move
obstacles (B. Renault’s PhD). First, we examined where obstacles should be optimally moved with regards
to space access (called Social-NAMO). We derived new spatial cost functions and NAMO algorithms
preserving accesses [111]. Second, we generalized the problem to n robots (called MR-NAMO). We
proposed a local coordination strategy exploiting Social-NAMO rearrangement properties and offering
online efficient plans (under review). For evaluation, we developped a simulator (S-NAMO-SIM) that we
share with the communauty.

Multi-robot exploration
An important part of our research is focused on designing new online planning solutions for multi-
robot systems to accomplish exploration and observation gathering tasks in complex and dynamic
environments. Problems related to computational and communication constraints are here crucial, and
distributed approaches are fundamental to overcome them and obtain robust solutions.

Following these goals, a particular attention has been given to the exploration/mapping of 3D envir-
onments with a team of aerial vehicles, for which we presented a new decentralized solution based on
the combination of a stochastic optimization approach with a more classic strategy that exploits frontier
points [114]. In [113], we then showed how this approach is part of a more general formulation that can
deal with the optimal deployment coverage as well, for which we also studied the impact of an offline
initialization step based on very partial information on the environment. Stochastic-based planning
methods have been also exploited to propose a new source seeking strategy [112]. This solution is based
on robust estimations of the signal gradient obtained by the robots while exploring the environment
in predefined symmetric formations [61] and that are then used to bias a correlated random walk that
guides the robots.

Connected to this subject, we also investigated multi-robot strategies to cover complex scenes/
environments, by introducing multi-resolution maps. This concerned human activity observation by
a set of ground robots ([99]) and more recently inspection of large structures with aerial robots (in the
framework of the European project BugWright2, in collaboration with LSL, Austria).

Swarm navigation
In the context of self-reorganized systems, or swarm robotics, we explore how collective navigation
can be defined and adapted to fleets of communicating robots. In A. Bonnefond’s PhD, we studied
flocking models, where each agent communicates locally its speed and position, and by simulating
radio propagation we examined how obstacles impact the flocking robustness. Then we extended and
combined two standard models, Olfati-Saber and Vásárhelyi, to improve their ability to stay connected
while evolving in environments with different obstacle distributions [59]. This work is supported by the
Inria/DGA Dynaflock project and by the national Equipex+ TIRREX infrastructure. Related to this work,
we address Controled Mobility where navigation must take into account the need of maintaining the fleet

Project CHROMA

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connectivity. In R. Grunblatt’s PhD, we proposed a distributed algorithm that optimizes communication
in a swarm of UAVs by changing their antenna orientation online [80]. This work is continued in the ANR
CONCERTO project.

2. Machine Learning

Machine learning techniques for decision-making are autonomous algorithms for computing behavi-
ors, also called policies or strategies, which describe what action an agent should take when at a given
state. Learning to act in complex environments requires knowledge about the structure of underlying
decision-making problems. We contribute in revealing insightful underlying structures, exploiting them
within existing or customized algorithms, and showing they improve performances in a wide variety of
decision-making problems. For instance, optimal policies may have concise representations of their
mental states, e.g., sufficient statistics, ego-centric neural-network architectures.

In the context of POMDPs9, dec-POMDPs, zs-POSGs10, and st-POSGs, we have identified a unified
framework for solving these problems [70]. Our methodological framework splits into five steps. First, we
have shown that all these problems are convertible into a single problem called a common-knowledge
game [70]. Then, we recast the (possibly discrete) problem into a continuous one. Furthermore, we
establish uniform continuity properties for general and hierarchical dec-POMDPs. We proved that optimal
value functions for general and hierarchical dec-POMDPs are PWLC [70, 128]. We also established optimal
value functions for general zs-POSGs that are Lipschitz continuous [62, 63]. Finally, we designed planning
algorithms for games against Nature [77], pure competitive games [64], as well as for pure cooperative
games [70]. We extended exact and deep reinforcement learning algorithms for general dec-POMDPs
[70]. We finally conducted applications of deep reinforcement along two main directions: autonomous
transportation systems [60]; and automatic control systems [130].

Creating agents capable of high-level reasoning based on structured memory was the main topic
of C. Wolf during its Inria "delegation" in Chroma (2017-19). In the PhD thesis of E. Beeching we pro-
posed EgoMap, a spatially structured metric neural memory architecture in deep reinforcement learning.
Through visualizations, the agent learns to map relevant objects in its spatial memory without any super-
vision purely from reward, see [58] (ICPR), [57] (ECCV) and [56] (ECML). In late 2019, C. Wolf obtained
the AI Chair "REMEMBER", involving O. Simonin and J. Dibangoye from Chroma and L. Matignon from
LIRIS lab/Univ Lyon 1. In this context, the PhD of P. Marza proposes to use an implicit representation of
the environment to navigate towards targets. Neural fields are a recent category of approaches where
the geometry, and eventually the semantics, of a 3D scene are encapsulated into the weights of a trained
neural network.

As mentioned in Axis I, making appropriate decisions for safe robot / vehicle navigation is highly
dependent on the results of the perception step. Indeed, the critical elements of a complex dynamic scene
must first be extracted, classified and tracked in real time, by combining Bayesian and AI approaches to
build "semantic grids" [89] [92, 73, 71]. Two types of decision-making based on RL were developed for
autonomous driving on highways and road junctions respectively, as part of the doctoral theses of David
Sierra-Gonzalez [121] and Mathieu Barbier [53].

A last activity concerns our involvement in the RoboCup11 competition. In 2017 we founded the
LyonTech team12 to participate to the RoboCup@Home challenge within the Pepper league. We worked
on learning and integrating high-level functionalities such as object and people recognition, mapping
and planning in indoor environments (see [82]). We reached the 5th place in 2018, the 3rd place in 2019,
the 2d place in 2021, and awarded the best scientific paper at the RoboCup conference in 2019 [118].

3. Offline constrained optimisation problems

In this theme, we study and design algorithms for solving NP-hard constrained optimization problems
(COPs) such as vehicle routing problems, or multi-agent path finding problems. We more particularly
study approaches based on Constraint Programming (CP), that provides declarative languages for de-

9Partially Observable Markov Decision Processes, dec: decentralized
10Partially Observable Stochastic Games, zs: 2-player zero-sum
11RoboCup is an annual international robotics competition proposed and founded in 1996 by a group of university professors

(the major competition of Robotics in the world) www.robocup.org/.

12robocup-lyontech.github.io

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Inria Annual Report 2023

scribing these problems by means of constraints, and generic algorithms for solving them by means of
constraint propagation.

In the context of the European project BugWright2 and the PhD thesis of Xiao Peng (started in Nov.
2020), we study a new multi-agent path finding problem for tethered robots, that are attached with cables
to anchor points. In [106], we show how to compute bounds for this problem by solving well known
assignment problems, and we introduce a CP model for solving the problem to optimality. In [105], we
extend this approach to the case of non point-sized robots.

In the context of the PhD thesis of Romain Fontaine (started in Oct. 2020), we study vehicle routing
problems with time-dependent cost functions (such that travel times between locations to visit may
change along the day), and we design new solving approaches based on an anytime extension of A* [78,
15]. We study the impact of time-window constraints on the satisfiability and the difficulty of routing
problems in [115].

Besides planning and routing problems which are at the core of Chroma topics, we also investigate

the interest of using CP solvers for solving cryptanalysis problems [116].

Collaborations

• Social navigation: collaboration with Julie Dugdale and Dominique Vaufreydaz, Asso. Prof. at LIG
Laboratory and with Prof. Philippe Martinet from Acentauri Inria team (common PhD students
and projects: ANR VALET, ANR HIANIC, ANR ANNAPOLIS).

• Multi-robot exploration: Prof. Cedric Pradalier from GeorgiaTech Metz/CNRS (France) leading the

European project BugWright2 — Prof. Luca Schenato from University of Padova (Italy).

• Swarm of UAVs: Prof. Isabelle Guérin-Lassous from LIP lab/Inria DANTE team (common PhD
students and projects: ANR CONCERTO, INRIA/DGA DynaFlock) — Research Director Isabelle
Fantoni from CNRS/LS2N Lab in Nantes (common projects: ANR CONCERTO, Equipex TIRREX) —
Dr. Melanie Schranz, senior researcher at LakeSide Lab, Austria (BugWright2 project).

• Machine Learning: strong collaborations with Christian Wolf, Naver Labs Europe & LIRIS lab/INSA13
(common PhD students and projects: ANR DELICIO, ANR AI Chair REMEMBER) and with Laetitia
Matignon, Asso. Prof. at LIRIS lab/Univ Lyon 1 (common projects and Phd/Master students) —
Akshat Kumar, Asso. Prof. at School of Computing and Information Systems (Singapore) — Abdallah
Saffidine, Research Asso. at University of New South Wales (Sydney) — Prof. Stéphane Canu at INSA
Rouen — Olivier Buffet, Vincent Thomas and François Charpillet, Inria researchers in LARSEN
team, Nancy (common publications and project ANR PLASMA).

• Semantic grids for autonomous navigation: Collaboration with Toyota Motor Europe (TME), R&D

center Zaventen (Brussels). R&D contracts, common publications and patents.

• Constrained optimization: Omar Rifki (Post-doc) and Thierry Garaix (Ass. Prof) at LIMOS Ecole des
Mines de Saint Etienne — Pascal Lafourcade and François Delobel, Asso. Prof. at LIMOS Université
Clermont-Ferrand.

4 Application domains

4.1 Introduction

Applications in Chroma are organized in two main domains : i) Future cars and transportation of persons
and goods in cities, ii) Service robotics with ground and aerial robots. These domains correspond to the
experimental fields initiated in Grenoble (eMotion team) and in Lyon (CITI lab). However, the scientific
objectives described in the previous sections are intended to apply equally to these applicative domains.
Even if our work on Bayesian Perception is today applied to the intelligent vehicle domain, we aim to
generalize to any mobile robots. The same remark applies to the work on multi-agent decision making.
We aim to apply algorithms to any fleet of mobile robots (service robots, connected vehicles, UAVs). This
is the philosophy of the team since its creation.

13Christian Wolf has just joined Naver Labs Europe in January 2022, leaving LIRIS & INSA Lyon.

Project CHROMA

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Figure 2: Most of the Chroma platforms: the equipped Toyota Lexus, the autonomous version of the
Renault Zoe car, the Pepper humanoid, the PX4-Vision UAV.

4.2 Future cars and transportation systems

Thanks to the introduction of new sensor and ICT technologies in cars and in mass transportation
systems, and also to the pressure of economical and security requirements of our modern society, this
application domain is quickly changing. Various technologies are currently developed by both research
and industrial laboratories. These technologies are progressively arriving at maturity, as it is witnessed by
the results of large scale experiments and challenges such as the Google’s car project and several future
products announcements made by the car industry. Moreover, the legal issue starts to be addressed in
USA (see for instance the recent laws in Nevada and in California authorizing autonomous vehicles on
roads) and in several other countries (including France).

In this context, we are interested in the development of ADAS 14 systems aimed at improving comfort
and safety of the cars users (e.g., ACC, emergency braking, danger warnings), of Fully Autonomous
Driving functions for controlling the displacements of private or public vehicles in some particular
driving situations and/or in some equipped areas (e.g., automated car parks or captive fleets in downtown
centers or private sites), and of Intelligent Transport including optimization of existing transportation
solutions.

Since about 8 years, we are collaborating with Toyota and with Renault-Nissan on these applications
(bilateral contracts, PhD Theses, shared patents), but also recently with Volvo group (2016-20). We are
also strongly involved (since 2012) in the innovation project Perfect then now Security for autonomous
vehicle of the IRT 15 Nanoelec (transportation domain). Since 2016, we have been awarded two European
projects (the "ENABLE" H2020 ECSEL project 16 and the "CPS4EU" H2020 ECSEL project) involving
major European automotive constructors and car suppliers. In these projects, Chroma is focusing on the
embedded perception component (models and algorithms, including the certification issue). Chroma
(A. Spalanzani) led also the ANR Hianic (2018-21) dealing with pedestrian-vehicle interaction for a safe
navigation.

In this context, Chroma has two experimental vehicles equipped with various sensors (a Toyota Lexus
and a Renault Zoe. , see. Fig. 2) , which are maintained by Inria-SED 17 and that allow the team to carry
out experiments in realistic traffic conditions (Urban, road and highway environments). The Zoe car has
been automated in December 2016, through our collaboration with the team of P. Martinet (IRCCyN Lab,
Nantes), that allow new experiments in the team.

4.3 Service robotics with ground and aerial robots

Service robotics is an application domain quickly emerging, and more and more industrial companies
(e.g., IS-Robotics, Samsung, LG) are now commercializing service and intervention robotics products
such as vacuum cleaner robots, drones for civil applications, entertainment robots, etc. One of the
main challenges is to propose robots which are sufficiently robust and autonomous, easily usable by
non-specialists, and marked at a reasonable cost. We are involved in developing fleet of ground robots

14Advanced Driver Assistance Systems
15Institut de Recherche Technologique
16ENABLE-S3: European Initiative to Enable Validation for Highly Automated Safe and Secure Systems.
17Service Expérimentation et Développement

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Inria Annual Report 2023

and aerial ones, see Fig. 2. Since 2016, we study solutions for 3D observation/exploration of complex
scenes or environments with a fleet of UAVs (eg. BugWright2 H2020 project, Dynaflock Inria/DGA project)
or ground robots (COMODYS FIL project [99]).

A more recent challenge for the coming decade is to develop robotized systems for assisting elderly
and/or disabled people. In the continuity of our work in the IPL PAL 18, we aim to propose smart
technologies to assist electric wheelchair users in their displacements and also to control autonomous
cars in human crowds (see Figure 4 for illustration). This concerns our recent "Hianic" ANR project.
Another emerging application is humanoid robots helping humans at their home or work. In this context,
we address the problem of NAMO (Navigation Among Movable Obstacles) in indoor environments
(see PhD of B. Renault started on 2018). More generally we address navigation and object search with
humanoids robots such as Pepper, in the context of the RoboCup-Social League competition.

5 Social and environmental responsibility

5.1 Footprint of research activities

• Some of our research topics involve high CPU usage. Some researchers in the team use the Grid5000
computing cluster, which guarantees efficient management of computing resources and facilitates
the reproducibility of experiments. Some researchers in the team keep a log summarizing all the
uses of computing servers in order to be able to make statistics on these uses and assess their
environmental footprint more finely.

5.2 Impact of research results

• Romain Fontaine’s thesis aims to develop algorithms for the efficient optimization of delivery
rounds in the city. Our work is motivated by taking into account environmental constraints, set out
in the report "Assurer le fret dans un monde fini" from The Shift Project (theshiftproject.org/wp-
content/uploads/2022/03/Fret_rapport-final_ShiftProject_PTEF.pdf)

• Léon Fauste’s thesis focuses on the design of a decision support tool to choose the scale when
relocating production activities (work in collaboration with the Inria STEEP team). This resulted in
a publication in collaboration with STEEP at ROADEF [66] and the joint organization of a workshop
on the theme "Does techno-solutionism have a future?" during the Archipel conference which has
been organized in June 2022 (archipel.inria.fr/).

• Christine Solnon has co-organized in Lyon (with Sylvain Bouveret, Nadia Brauner, Pierre Fouilhoux,
Alexandre Marié, and Michael Poss), a one day workshop on environmental and societal challenges
of decision making, on the 17th of November (sponsored by GDR RO and GDR IA).

• Chroma will organize in 2024 the national Archipel conference (archipel.inria.fr) on anthropocene

challenges.

• Some of us are strongly involved in the development of INSA Lyon training courses to integrate
DDRS issues (Christine Solnon is a member of the CoPil and the GTT on environmental and social
digital issues). This participation feeds our reflections on the subject.

6 Highlights of the year

6.1 Awards

• INSA Lyon medal 2022 for Fabrice Jumel, Benoit Renault, Jacques Saraydaryan and Olivier Simonin:
on 21/02/2023, they received the INSA Lyon medal for their results in the international RoboCup
competition (2021-23).

18Personnaly Assisted Living

Project CHROMA

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• Best poster at NaverLabs 2023 - Europe International Workshop on AI for Robotics, Nov 2023,

Grenoble, France, for Rabbia Ashgar [44].

6.2 Team life

• Jilles Dibangoye defended his HdR on 23 June 2023 in CITI Lab. ("Partially Observable Stochastic
Games : An introduction to common-knowledge dynamic programming"). He left the team in July
to join the University of Groningen (Netherlands) as Full Professor.

• Visit of Professor François Michaud, from Univ. Sherbrooke - 3IT Institute - IntRoblab, 20/5/2023

to 2/06/2023, in Lyon, Chroma team.

6.3 New projects

• ANR TSIA, project ’VORTEX’ : Vision-based Reconfigurable Drone Swarms for Fast Exploration.
Partners : CITI (Chroma), LS2N (Nantes), ENSTA (Paris), ONERA (Paris), Lab-STICC (Brest). 694 K€.
PI : Olivier Simonin.

• ANR Challenge MOBILEX (project Navir) for MOBILity in complEX environnements. Partners :

Inria Chroma, HAWAI.tech (coord.). 250K€. Chroma’s coord. : Lukas Rummelhard.

7 New software, platforms, open data

7.1 New software

7.1.1 CMCDOT

Functional Description: CMCDOT is a Bayesian filtering system for dynamic occupation grids, allowing
parallel estimation of occupation probabilities for each cell of a grid, inference of velocities, pre-
diction of the risk of collision and association of cells belonging to the same dynamic object. Last
generation of a suite of Bayesian filtering methods developed in the Inria eMotion team, then in the
Inria Chroma team (BOF, HSBOF, ...), it integrates the management of hybrid sampling methods
(classical occupancy grids for static parts, particle sets for parts dynamics) into a Bayesian unified
programming formalism , while incorporating elements resembling the Dempster-Shafer theory
(state "unknown", allowing a focus of computing resources). It also offers a projection system of
the estimated scene in the near future, to reference potential collisions with the ego-vehicle or any
other element of the environment, as well as very low cost pre-segmentation of coherent dynamic
spaces (taking into account speeds). It takes as input instantaneous occupation grids generated
by sensor models for different sources, the system is composed of a ROS package, to manage the
connectivity of I / O, which encapsulates the core of the embedded and optimized application on
GPU Nvidia (Cuda), allowing real-time analysis of the direct environment on embedded boards
(Tegra X1, X2). ROS (Robot Operating System) is a set of open source tools to develop software
for robotics. Developed in an automotive setting, these techniques can be exploited in all areas
of mobile robotics, and are particularly suited to highly dynamic and uncertain environment
management (e.g. urban scenario, with pedestrians, cyclists, cars, buses, etc.).

Authors: Amaury Nègre, Lukas Rummelhard, Jean-Alix David, Christian Laugier

Contact: Christian Laugier

7.1.2 Ground Elevation and Occupancy Grid Estimator (GEOG - Estimator)

Keywords: Robotics, Environment perception

Functional Description: GEOG-Estimator is a system of joint estimation of the shape of the ground,
in the form of a Bayesian network of constrained elevation nodes, and the ground-obstacle clas-
sification of a pointcloud. Starting from an unclassified 3D pointcloud, it consists of a set of

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expectation-maximization methods computed in parallel on the network of elevation nodes, in-
tegrating the constraints of spatial continuity as well as the influence of 3D points, classified as
ground-based or obstacles. Once the ground model is generated, the system can then construct
a occupation grid, taking into account the classification of 3D points, and the actual height of
these impacts. Mainly used with lidars (Velodyne64, Quanergy M8, IBEO Lux), the approach can
be generalized to any type of sensor providing 3D pointclouds. On the other hand, in the case
of lidars, free space information between the source and the 3D point can be integrated into the
construction of the grid, as well as the height at which the laser passes through the area (taking into
account the height of the laser in the sensor model). The areas of application of the system spread
across all areas of mobile robotics, it is particularly suitable for unknown environments. GEOG-
Estimator was originally developed to allow optimal integration of 3D sensors in systems using 2D
occupancy grids, taking into account the orientation of sensors, and indefinite forms of grounds.
The ground model generated can be used directly, whether for mapping or as a pre-calculation step
for methods of obstacle recognition or classification. Designed to be effective (real-time) in the
context of embedded applications, the entire system is implemented on Nvidia graphics card (in
Cuda), and optimized for Tegra X2 embedded boards. To ease interconnections with the sensor
outputs and other perception modules, the system is implemented using ROS (Robot Operating
System), a set of opensource tools for robotics.

Authors: Amaury Nègre, Lukas Rummelhard, Lukas Rummelhard, Jean-Alix David, Christian Laugier

Contact: Christian Laugier

7.1.3 Zoe Simulation

Name: Simulation of INRIA’s Renault Zoe in Gazebo environment

Keyword: Simulation

Functional Description: This simulation represents the Renault Zoe vehicle considering the realistic
physical phenomena (friction, sliding, inertia, ...). The simulated vehicle embeds sensors similar to
the ones of the actual vehicle. They provide measurement data under the same format. Moreover
the software input/output are identical to the vehicle’s. Therefore any program executed on the
vehicle can be used with the simulation and reciprocally.

Authors: Christian Laugier, Nicolas Turro, Thomas Genevois

Contact: Christian Laugier

7.1.4 Hybrid-state E*

Name: Path planning with Hybrid-state E*

Keywords: Planning, Robotics

Functional Description: Considering a vehicle with the kinematic constraints of a car and an environ-
ment which is represented by a probabilistic occupancy grid, this software produces a path from
the initial position of the vehicle to its destination. The computed path may include, if necessary,
complex maneuvers. However the suggested path is often the simpler and the shorter.

This software is designed to take benefit from bayesian occupancy grids such as the ones computed
by the CMCDOT software.

URL: https://team.inria.fr/chroma/en/

Authors: Christian Laugier, Thomas Genevois

Contact: Christian Laugier

Partner: CEA

Project CHROMA

7.1.5 Pedsim_ros_AV

Name: Pedsim_ros_AV

17

Keywords: Simulator, Multi-agent, Crowd simulation, Autonomous Cars, Pedestrian

Scientific Description: These ROS packages are useful to support robotic developments that require
the simulation of pedestrians and an autonomous vehicle in various shared spaces scenarios.
They allow: 1. in simulation, to pre-test autonomous vehicle navigation algorithms in various
crowd scenarios, 2. in real crowds, to help online prediction of pedestrian trajectories around the
autonomous vehicle.

Individual pedestrian model in shared space (perception, distraction, personal space, pedestrians
standing, trip purpose). Model of pedestrians in social groups (couples, friends, colleagues, family).
Autonomous car model. Pedestrian-autonomous car interaction model. Definition of shared space
scenarios: 3 environments (business zone, campus, city centre) and 8 crowd configurations.

Functional Description: Simulation of pedestrians and an autonomous vehicle in various shared space
scenarios. Adaptation of the original Pedsim_ros model to simulate heterogeneous crowds in
shared spaces (individuals, social groups, etc.). The car model is integrated into the simulator and
the interactions between pedestrians and the autonomous vehicle are modeled. The autonom-
ous vehicle can be controlled from inside the simulation or from outside the simulator by ROS
commands.

URL: https://github.com/maprdhm/pedsim_ros_AV

Publications: hal-02194735, hal-02514963

Contact: Manon Predhumeau

Participants: Manon Predhumeau, Anne Spalanzani, Julie Dugdale, Lyuba Mancheva

Partner: LIG

7.1.6 S-NAMO-SIM

Name: S-NAMO Simulator

Keywords: Simulation, Navigation, Robotics, Planning

Functional Description: 2D Simulator of NAMO algorithms (Navigation Among Movable Obstacles) and

MR-NAMO (Multi-Robot NAMO), ROS compatible

Release Contributions: Creation

Authors: Benoit Renault, David Brown, Jacques Saraydaryan, Olivier Simonin

Contact: Olivier Simonin

7.1.7 SimuDronesGR

Name: Simultion of UAV fleets with Gazebo/ROS

Keywords: Robotics, Simulation

Functional Description: The simulator includes the following functionality : 1) Simulation of the mech-
anical behavior of an Unmanned Aerial Vehicle : * Modeling of the body’s aerodynamics with lift,
drag and moment * Modeling of rotors’ aerodynamics using the forces and moments’ expressions
from Philppe Martin’s and Erwan Salaün’s 2010 IEEE Conference on Robotics and Automation
paper "The True Role of Accelerometer Feedback in Quadrotor Control". 2) Gives groundtruth
informations : * Positions in East-North-Up reference frame * Linear velocity in East-North-Up
and Front-Left-Up reference frames * Linear acceleration in East-North-Up and Front-Left-Up

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Inria Annual Report 2023

reference frames * Orientation from East-North-Up reference frame to Front-Left-Up reference
frame (Quaternions) * Angular velocity of Front-Left-Up reference frame expressed in Front-Left-Up
reference frame. 3) Simulation of the following sensors : * Inertial Measurement Unit with 9DoF
(Accelerometer + Gyroscope + Orientation) * Barometer using an ISA model for the troposphere
(valid up to 11km above Mean Sea Level) * Magnetometer with the earth magnetic field declination
* GPS Antenna with a geodesic map projection.

Release Contributions: Initial version

Authors: Vincent Le Doze, Johan Faure, Olivier Simonin, Vincent Dufour

Contact: Olivier Simonin

Partner: Insa de Lyon

7.1.8 spank

Name: Swarm Protocol And Navigation Kontrol

Keyword: Protocoles

Functional Description: Communication and distance measurement in an uav swarm

URL: https://gitlab.inria.fr/dalu/spank

Contact: Stéphane D’Alu

Participant: Stéphane D’Alu

7.2 New platforms

7.2.1 Chroma Aerial Robots platform

Participants: Olivier Simonin, Alessandro Renzaglia, Johan Faure.

This platform is composed of :

• Four quadrirotor PX4 Vision UAVs (Unmaned Aerial Vehicles), acquired 4 in 2021 and 2022. This
platform is funded and used in the projects "Dynaflock" (Inria-DGA) and ANR "CONCERTO" (the
team also owns 5 Parrot Bebop UAVs).

• Two outdoor inflatable aviaries of 6m(L) x 4m(l) x 5m(H) each.

• Two indoor inflatable aviaries of 5m(L) x 3m(l) x 2.5m(H) each.

7.2.2 Barakuda

Participants:

Lukas Rummelhard, Thomas Genevois, Robin Baruffa, Hugo Bantig-
nies, Nicolas Turro.

Within the scope of the ANR Challenge MOBILEX, a 4-wheel all-terrain Barakuda robot from Shark
Robotics is provided, and will be equipped with sensors and compute capabilities to achieve increasing
level of autonomy.

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8 New results

8.1 Bayesian Perception

Participants:

Lukas Rummelhard, Thomas Genevois, Robin Baruffa, Hugo Ban-
tignies, Nicolas Turro, Jean-Baptiste Horel, Alessandro Renzaglia,
Anne Spalanzani, Christian Laugier.

8.1.1 Evolution of the CMCDOT framework

Participants: Robin Baruffa, Hugo Bantignies, Thomas Genevois, Nicolas Turro,

Lukas Rummelhard.

Recognized as one of the core technologies developed within the team over the years (see related
sections in previous activity report of Chroma, and previously e-Motion reports), the CMCDOT framework
is a generic Bayesian Perception framework, designed to estimate a dense representation of dynamic
environments and the associated risks of collision, by fusing and filtering multi-sensor data. This whole
perception system has been developed, implemented and tested on embedded devices, incorporating
over time new key modules. Now extended to 3D for grid fusion and incorporating semantic states in
the whole process, this framework, and the corresponding software, has continued in 2023 to be the
core of many important industrial partnerships and academic contributions, and to be the subject of
important developments, both in terms of research and engineering. Most work on the core technology
consisted this year on adaptations for the deployment on new experimental platforms and environments,
and software evolutions.

8.1.2 Cooperative Perception through communication with Dynamic Occupancy Grids

Participants: Hugo Bantignies, Nicolas Turro, Lukas Rummelhard.

In order to reduce the cost of deploying a fleet of automated mobile robots, or to enrich the perception
capacity of a mobile element in certain particularly accident-prone areas, the option of deploying differ-
ent sensors on an instrumented and communicating infrastructure is particularly attractive. Perception
methods based on Bayesian occupancy grid are well-suited for the fusion of heterogeneous and even
remote or mobile sensors, but the size of these structures makes their efficient communication a real chal-
lenge. Such an option is studied and tested within the CMCDOT framework, using V2X communication
(Vehicle to everything) to communicate dynamic probabilistic occupancy grids estimated by intelligent
infrastructure to a mobile robot. Recently, following the installation of a fixed infrastructure perception
station, a transmission of objects following a standard communication protocol (ETSI TR 103 562) was
implemented, using industrial V2X communication systems (e.g. Lacroix), while developing methods
for compressing and sending occupancy grids. After analysis of the geometric primitives supported by
the communication protocol, methods of decomposition of occupancy grids into simple transmissible
elements under constraint, composed of weighted objects and poligons, and their reconstruction and
adaptation after radio communication, was developed and tested, the objective being to reconstruct the
occupancy as faithfully as possible, with the smallest possible dataflow.

8.1.3 Generation of proper probabilistic occupancy grids by neural network

Participants:

Robin Baruffa, Lukas Rummelhard.

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Inria Annual Report 2023

In the CMCDOT framework, modules based on neural networks for the semantic analysis of the
environment in the image plane of the cameras had previously been developed and integrated, making
it possible to distinguish different types of occupancy ( pedestrians, vehicles, etc.) after reprojection
and fusion with probabilistic occupancy grids generated by other sensors. These modules were limited
to this semantic information, relying on other sensors for precise position estimation. New modules
was developed, integrating state-of-the-art neural networks (Lift Splat Shoot), allowing inference from
camera images of semantic occupancy grids, therefore directly the position of the different types of
semantic occupancy. The performance of the model was evaluated through various experiments, notably
on the Transpolis test space. In parallel, the integration of neural networks within a proper Bayesian
probabilistic framework were studied. If the generated semantic grids are particularly rich and interesting
to merge, offering a different modality from the other sensors (thus a decorrelated potential error), the
provided grid values should be reworked to properly correspond to same probilistic state estimations as
the other sensors. Statistical analysis encapsulating probabilistic models, and associated software, have
been initiated.

8.1.4 From Probabilistic Occupancy Grids to versatile Collision Avoidance using Predictive Collision

Detection

Participants:

Thomas Genevois, Lukas Rummelhard, Anne Spalanzani, Chris-
tian Laugier.

As robots are spreading in diverse environments, new collision avoidance challenges arise. Real world
collision avoidance often includes dynamic, complex, unstructured and uncertain environments. While
these constraints are rarely considered all together in perception and navigation research works, a broad
integration from perception to navigation in a versatile collision avoidance approach was developped,
designed to operate under these constraints. The proposed solution relies on a new Predictive Colli-
sion Detector, an interface between state-of-art grid-based perception and sampling-based planners.
Unlike most other approaches, ours operates only on elementary spatial occupancy, without object
segmentation, capturing the richness and versatility of modern occupancy grid perception.

Relying on the embedded Bayesian perception bricks developed within the CMCDOT framework,
these original navigation methods on adapted Bayesian occupancy grid allowed the automation of
different robotic platforms (automated Zoe, robot of industrial transit, light transport vehicle), in different
types of environments generally considered difficult (uncertain, noisy, unknown, complex, dynamic),
fully relying on the advantages provided by the representation in the form of a probabilistic occupancy
grid. This approach was the subject of a paper presented at the IEEE IV 2023 international conference
[30].

8.1.5 Validation of AI-based algorithms in autonomous vehicles

Participants:

Jean-Baptiste Horel, Alessandro Renzaglia, Radu Mateescu (Convecs),
Wendelin Serwe (Convecs), Christian Laugier.

In the last years, there has been an increasing demand for regulating and validating intelligent vehicle
systems to ensure their correct functioning and build public trust in their use. Yet, the analysis of
safety and reliability poses a significant challenge. More and more solutions for autonomous driving
are based on complex AI-based algorithms whose validation is particularly challenging to achieve. An
important part of our work has been recently devoted to tackle this problem, finding new suitable
approaches to validate probabilistic algorithms for perception and decision making in autonomous
driving, and investigating how simulations and experiments in controlled environment can help to
solve this challenge. This activity, started with our participation in the European project Enable-S3
(2016-2019), is now continuing with the PRISSMA project, started in April 2021, where we participate
in collaboration the the Inria Convecs team. This project, funded by the French government in the
framework of the “Grand Défi: Sécuriser, certifier et fiabiliser les systèmes fondés sur l’intelligence

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artificielle”, regroups several companies and public institutes in France and tackles the challenge of the
validation and certification of artificial intelligence based solutions for autonomous mobility.

Within this context, our main effort has been focused on proposing a new approach specifically
designed for perception component verification that, given a high-level and human-interpretable de-
scription of a critical situation, generates relevant AV scenarios and uses them for automatic verification.
To achieve this goal, we integrate two recently proposed methods for the generation and the verification
that are based on formal verification tools. First, we proposed the use of formal conformance test genera-
tion tools to derive, from a verified formal model, sets of scenarios to be run in a simulator. This work
was based on the results published in [81]. Second, we model check the traces of the simulation runs
to validate the probabilistic estimation of collision risks. Using formal methods brings the combined
advantages of an increased confidence in the correct representation of the chosen configuration (tem-
poral logic verification), a guarantee of the coverage and relevance of automatically generated scenarios
(conformance testing), and an automatic quantitative analysis of the test execution (verification and
statistical analysis on traces). accepted for pubblication in the Journal of Intelligent and Robotic Systems
[16].

8.1.6 Navigation-Based Evaluation Metric for Probabilistic Occupancy Grids

Participants:

Jean-Baptiste Horel, Robin Baruffa, Lukas Rummelhard, Aless-
andro Renzaglia, Christian Laugier.

With the goal of testing and validating perception algorithms, a critical step is to define appropriate
metrics to evaluate the system behavior. Despite the importance and broad use of probabilistic Occu-
pancy Grids (OG) in autonomous driving, most existing approaches to evaluate their reliability are based
on general-purpose metrics derived from the computer vision literature. In [32], we propose a new metric
that evaluates the similarity of two OGs: a Ground Truth (GT) of the environment and the inference
of the environment made by the perception system. This metric is specifically designed to assess the
similarity between OGs by considering the behavior of an ego vehicle navigating through the grids. The
main postulate is that if a navigation algorithm generates similar trajectories using two OGs, then the two
are similar for navigation purposes.

Simulating and comparing the navigation behavior instead of doing cell-wise comparisons directly
on the OGs gives this metric relevant properties. It is able to evaluate topological errors, it measures how
occupancy errors on the inference change the cost of the paths and how this affects the global topology
of the OG. It puts emphasis on cells that are most crossed by paths since their occupancy is incorporated
in more costs. In other words, these cells are topologically more relevant for navigation (e.g., areas closer
to the ego vehicle or bottlenecks). Furthermore, this metric is well suited to evaluate uncertainty: paths
tend to avoid uncertainty whenever possible or cross it otherwise, but in both cases, the navigation cost
is increased.

8.2 Situation Awareness & Decision-making for Autonomous Vehicles

Participants: Christian Laugier, Manuel Alejandro Diaz-Zapata, Alessandro Ren-
zaglia,
Jilles Dibangoye, Anne Spalanzani, Wenqian Liu, Ab-
hishek Tomy, Khushdeep Singh Mann, Rabbia Asghar, Lukas Rum-
melhard, Gustavo Salazar-Gomez, Olivier Simonin.

In this section, we present all the novel results in the domains of perception, motion prediction and

decision-making for autonomous vehicles.

8.2.1 Situation Understanding and Motion Forecasting

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Inria Annual Report 2023

Participants: Kaushik Bhowmik, Anne Spalanzani.

Forecasting the motion of surrounding traffic is one of the key challenges in the quest to achieve
safe autonomous driving technology. Current state-of-the-art deep forecasting architectures are capable
of producing impressive results. However, in many cases, they also output completely unreasonable
trajectories, making them unsuitable for deployment. In 2022, we have developed a deep forecasting
architecture that leverages the map lane centerlines available in recent datasets to predict sensible
trajectories; that is, trajectories that conform to the road layout, agree with the observed dynamics of the
target, and react to the presence of surrounding agents. To model such sensible behavior, the proposed
architecture first predicts the lane or lanes that the target agent is likely to follow. Then, a navigational
goal along each candidate lane is predicted, allowing the regression of the final trajectory in a lane- and
goal-oriented manner. Our experiments in the Argoverse dataset show that our architecture achieves
performance on-par with lane-oriented state-of-the-art forecasting approaches and not far behind goal-
oriented approaches, while consistently producing sensible trajectories. This work was published at IEEE
ITSC 2022 [125].

In 2023 we started to work on predicting the motion of heterogeneous agents in urban environment (K.
Bhowmik’s PhD). The goal is to predict the behavior of pedestrians, cyclists, and even electrical scooters.
Using Deeplearning methods a existing big datasets of urban scenes, we plan to build robusts models of
various agents. When their motion is highly unpredictable, we aim at warning the autonomous vehicle
that a specific zone around it could be dangerous.

8.2.2 Semantic Grid Generation from Camera and LiDAR Sensor Fusion

Participants: Manuel Alejandro Diaz-Zapata, David Sierra-González, Chris-

tian Laugier, Jilles Dibangoye, Olivier Simonin.

Following with the work towards semantic grid generation, during 2023, the LAPTNet and LAPTNet-
FPN-PP methods [69] were further fine-tuned for vehicle detection, these results were presented at IEEE
ICRA 2023 [28]. Later, these models were also trained to perform Panoptic Segmentation, achieving
interesting results over the state of the art, keeping a good segmentation performance at an improved
inference speed compared to the state of the art models.

After this, a cross-dataset training and evaluation framework was developed as a manner to address
the gap in performance that occurs when deploying a model in a real platform. This framework allows
for the use of two or more datasets, for training and/or evaluation in the tasks of semantic grid and
panoptic grid generation. Experiments across state of the art methods showed that across datasets the
segmentation performance decreased, where the models that use LiDAR data showed the worst cross-
dataset perfomance. Interestingly, these experiments also showed that performance can be improved on
one dataset when adding extra training information from another dataset.

8.2.3 Lidar-RGB Sensor Fusion with Transformers for Semantic Grid Prediction

Participants: Gustavo Salazar-Gomez, David Sierra-González, Manuel Ale-
jandro Diaz-Zapata, Wenqian Liu, Christian Laugier.

The ability of perception systems in autonomous vehicles to accurately interpret and respond to the
surrounding environment is prime important for ensuring safety and efficiency. One significant challenge
that these systems face is the presence of occlusions, where objects or obstacles obstruct the line of sight.
Tackling occlusion scenarios is both crucial and challenging. Achieving robust environmental perception
is vital for effectively managing occlusions and assisting in navigation. Cutting-edge models integrate
data from LiDAR and cameras to deliver remarkable perception outcomes; however, the identification of
obstructed objects remains a persistent challenge. We emphasize the vital significance of temporal cues

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Figure 3: Illustration of a complex traffic situation in the occupancy grid framework

in enhancing robustness against occlusions within the birds-eye-view (BEV) semantic grid segmentation
task. We propose a framework that enables the processing of temporal multi-step inputs, where the
input at each time step comprises the spatial information encoded from fusing LiDAR and camera sensor
readings, using cross-attention and self-attention as means to fuse the features from these.

Furthermore, by achieving forecasting of multiple agents in the scene and handling the occlusions,
we propose an additional architecture to predict the trajectory of the ego vehicle, based on the inter-
action with the obstacles in the environment, using as input semantic grids. This was completed by
experimenting in the cutting-edge nuPlan dataset, a new framework proposed to be the first benchmark
for autonomous vehicle planning. In the next steps, we explore the options and needs for training an
end-to-end framework, capable of receiving as input the raw data from the LiDAR and camera sensors,
and output the trajectory planning of the autonomous vehicle to avoid obstacles in the scene.

8.2.4 Dynamic Scene Prediction for Urban Traffic Scene Using Occupancy Grid Maps

Participants: Rabbia Asghar, Lukas Rummelhard, Anne Spalanzani, Chris-

tian Laugier.

Prediction of dynamic environment is crucial to safe navigation for an autonomous vehicle. This,
however, is a challenging task due to uncertainty in the sensor data, the non-deterministic nature of
future, and complex behavior of agents. Initial work in 2022 proposed to represent the traffic scene in a
fixed frame as allo-centric occupancy grid and incorporate deep learning video prediction networks to
improve scene prediction. This work was presented at the ICARCV22 conference [52] and won the best
paper prize.

Carrying forward the work on allo-centric grid prediction, in 2023, we continue to tackle the prediction
problem by representing the scene as dynamic occupancy grid maps (DOGMs), associating semantic
labels to the occupied cells and incorporating map information. We propose a novel framework that
combines deep-learning-based spatio-temporal and probabilistic approaches to predict vehicle behavi-
ors. Contrary to the conventional OGM prediction methods, evaluation of our work is conducted against
the ground truth annotations. We experiment and validate our results on real-world NuScenes dataset
and show that our model shows superior ability to predict both static and dynamic vehicles compared
to OGM predictions. This work was presented to the IROS23 conference [24]. Moreover, Rabbia Asghar
presented a poster at Naverlab workshop and won the price of the best poster [44]. In next steps, we
continue to work on a real-time analysis of complex traffic situations in the occupancy grid framework in
order to predict all the scene. (see Fig. 3)

8.2.5 Temporal Multi-Modality Fusion Towards Occlusion-Aware Semantic Segmentation

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Inria Annual Report 2023

Participants: Wenqian Liu, Manuel Alejandro Diaz-Zapata, David Sierra-González,

Christian Laugier.

In the context of our cooperation with Toyota Motor Europe, we study new deep learning approaches
to understand the surrounding scenes of the autonomous vehicle. More particularly, we focus on
occlusion-aware bird’s-eye view (BEV) semantic segmentaion by proposing a novel sptio-temporal
approach.

Our main output in 2023 is an innovative model designed to process sequences of multi-modal
sensor data inputs, capturing the temporal memory of the scene and objects to enhance occlusion-aware
semantic bird’s-eye-view (BEV) grid segmentation. At the input stage, our approach extracts spatial
features from fused LiDAR and camera sensor readings at each time step throughout the temporal
sequence. We also use this model to experiment on downstream tasks such as to predict multi-step future
bird’s-eye view (BEV) grids in a one-shot manner and to forecast the future trajectory of the ego-vehicle.
This model also paths our way into end-to-end trainable framework for full-stack driving tasks for our
future research. This work is currently pending for publication.

8.3 Robust state estimation (Sensor fusion)

Participants:

Agostino Martinelli.

This research is the follow up of Dr Martinelli’s investigations carried out during the last ten years,
which mainly consisted in the derivation of the algebraic solution of the unknown input observability
problem. This was an open problem, formulated during the 1960’s by the automatic control community
and that remained unsolved for half century. The solution of this fundamental open problem, together
with all the analytic derivations based on the Ricci calculus with tensor fields, was published in a book [98]
and in a long paper by the Information Fusion journal [97], starting from the previous results published
in [96] and [95].

Starting from these results and derivations, during 2023, Dr Martinelli has found the general solution
of an even more important open problem, also in this case investigated by many scientists from different
scientific communities during the last half-century. This is the problem of obtaining the general algebraic
condition to study the identifiability of nonlinear ODE models in the presence of time varying parameters.
Identifiability describes the possibility of determining the values of the unknown parameters that charac-
terize a dynamic system from the knowledge of its inputs and outputs. During 2023, Dr Martinelli has
found the general algebraic condition that fully characterizes this property. This is the first ever condition
that can be successfully applied in the nonlinear case even in the presence of time varying parameters.
In addition, the model can also be time varying. In the presence of time varying parameters, it is only
required that their time dependence be analytical. The use of this condition requires no inventiveness
from the user as it simply needs to follow the steps of a systematic procedure that only requires to perform
the calculation of derivatives and matrix ranks. Time varying parameters are treated as unknown inputs
and their identifiability is based on the solution of the unknown input observability problem.

The condition was used to study several popular nonlinear biological models in the framework of viral
dynamics and for models that characterize the genetic toggle switch. In both cases, the condition allowed
automatically finding new fundamental results that reveal serious errors in the current state of the art.

The results of these investigations are available in [42], [41], and [43]. These documents also include a
careful analysis of the aforementioned errors in the current literature, highlighting that they are mainly
due to the inadequacy of existing methods in determining identifiability in the presence of time varying
parameters.

Unfortunately, publishing these results in leading journals has been impossible. In this regard, Section
4 of [41] provides one of the feedbacks obtained during the numerous review processes. In particular, this
section provides a thorough analysis of the feedback obtained from the editor (Marlis Hochbruck) of the
Survey and Review section of the SIAM Review Journal (SIREV) and from the two reviewers selected by
the editor. This clearly highlights the poor technical ability of the two reviewers and the inadequacy of the

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Figure 4: a. Illustration of a typical scene of an AV sharing its space with pedestrians. b. Illustration of a
zoe navigate a simulted crowd using augmented reality.

editor both for her poor technical ability in carrying out simple and basic calculations and her ineptitude
in taking a final decision. This is a serious problem. Dr. Martinelli is available to provide more details on
the many reviews beyond that discussed in Section 4 of [41].

8.4 Online planning and learning for robot navigation

8.4.1 Motion-planning in dense pedestrian environments

Participants:

Thomas Genevois, Anne Spalanzani.

Under the coordination of Anne Spalanzani, we study new motion planning algorithms to allow
robots/vehicles to navigate in human populated environment while predicting pedestrians’ motions.
We model pedestrians and crowd behaviors using notions of social forces and cooperative behavior
estimations. We investigate new methods to build safe and socially compliant trajectories using theses
models of behaviors. We propose proactive navigation solutions as well as deep learning ones.

since 2018 we’ve been working on modelling crowds and autonomous vehicles using Extended
Social Force Models (in the scope of Manon Predhumeau’s PhD) and Proactive Navigation for navigating
dense human populated environments (in the scope of Maria Kabtoul’s PhD). The focus of the first
work has been on the realistic simulation of crowds in shared spaces with an autonomous vehicle (AV),
see Fig. 4.a. We proposed an agent-based model for pedestrian reactions to an AV in a shared space,
based on empirical studies and the state of the art. The model includes an AV with Renault Zoé car’s
characteristics, and pedestrians’ reactions to the vehicle. The model extends the Social Force Model
with a new decision model, which integrates various observed reactions of pedestrians and pedestrians
groups. The SPACiSS simulator is an opensource simulator still used by the team. The focus of the second
work was a pedestrian-vehicle interaction behavioral model and a proactive navigation algorithm. The
model estimates the pedestrian’s cooperation with the vehicle in an interaction scenario by a quantitative
time-varying function. Using this cooperation estimation the pedestrian’s trajectory is predicted by a
cooperation-based trajectory planning model. We then used this cooperation based behavioral model
to design a proactive longitudinal velocity controller. In 2020, we integrated the previous navigation
system with the SPACiSS simulator under ROS (see Fig. 4.b). Next, we worked on a proactive and human-
like maneuvering system to deploy the steering control part of the navigation system. This work was
published in ICRA’22 [84] and in 2023 [17].

In 2023, Thomas Genevois worked on improving the previous works done in this topic. He proposed
some improvement on the SPACiSS simulator, proposed a version of a huamn and interaction aware
collision avoidance that can be used in the global architecture of the zoe car. He then linked the SPACiSS
simulator to his Augmented Reality simulator so that the navigation strategies among crowds can be
tested using the real zoé car [31].

8.4.2 Attention Graph for Multi-Robot Social Navigation with Deep Reinforcement Learning

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Participants:

Jacques Saraydaryan, Laetitia Matignon (Lyon1/LIRIS), Erwan Escudie
(Master internship), Olivier Simonin.

Learning robot navigation strategies among pedestrian is crucial for domain based applications.
Combining perception, planning and prediction allows us to model the interactions between robots
and pedestrians, resulting in impressive outcomes especially with recent approaches based on deep
reinforcement learning (RL). However, these works do not consider multi-robot scenarios.

In this work, we define MultiSoc, a new method for learning multi-agent socially aware navigation
strategies using RL (see illustration fig. 5.a). Inspired by recent works on multi-agent deep RL, our method
leverages graph-based representation of agent interactions, combining the positions and fields of view
of entities (pedestrians and agents). Each agent uses a model based on two Graph Neural Network
combined with attention mechanisms. First an edge-selector produces a sparse graph, then a crowd
coordinator applies node attention to produce a graph representing the influence of each entity on the
others. This is incorporated into a model-free RL framework to learn multi-agent policies. We evaluated
our approach on simulation and provide a series of experiments in a set of various conditions (number
of agents/pedestrians). Empirical results show that our method learns faster than social navigation
deep RL mono-agent techniques, and enables efficient multi-agent implicit coordination in challenging
crowd navigation with multiple heterogeneous humans. Furthermore, by incorporating customizable
meta-parameters, we can adjust the neighborhood density to take into account in our navigation strategy.
This work has been accepted for publication in AAMAS 2024 [29].

8.4.3 Deep-Learning for vision-based navigation

Participants:

Pierre Marza (PhD. student CITI & LIRIS), Christian Wolf (NaverLabs
europe), Olivier Simonin, Laetitia Matignon (LIRIS/Lyon 1).

Our goal is the automatic learning of robot navigation in complex environments based on specific
tasks and from visual input. The robot automatically navigates in the environment in order to solve a
specific problem, which can be posed explicitly and be encoded in the algorithm (e.g. find all occurences
of a given object in the environment, or recognize the current activities of all the actors in this environ-
ment) or which can be given in an encoded form as additional input, like text. Addressing these problems
requires competences in computer vision, machine learning/AI, and robotics (navigation and paths
planning).

A critical part for solving these kind of problems involving autonomous agents is handling memory
and planning. Many control problems in partially observed 3D environments involve long term depend-
encies and planning. Solving these problems requires agents to learn several key capacities: spatial
reasoning (exploring the environment and learning spatio-temporal regularities) and semantic mapping
(discovering semantics from interactions). While solutions exist for semantic mapping and semantic
SLAM [65, 126], a more interesting problem arises when the semantics of objects and their affordances
are not supervised, but defined through the task and thus learned from reward.

PhD thesis of Pierre Marza (since 2021). In the context of the REMEMBER AI Chair, Pierre Marza’s PhD
work is focusing on high-level navigation skills for autonomous agents in 3D environments (supervised by
C. Wolf, L. Matignon and O. Simonin). A key question is the ability to learn representations with minimal
human intervention and annotation. In 2023, we proposed to structure neural networks with two neural
implicit representations, which are learned dynamically during each episode and map the content of the
scene: (i) the Semantic Finder predicts the position of a previously seen queried object; (ii) the Occupancy
and Exploration Implicit Representation encapsulates information about explored area and obstacles.
Both representations are leveraged by an agent trained with Reinforcement Learning (RL) and learned
online during each episode. We evaluated the agent on Multi-Object Navigation and showed the high
impact of using neural implicit representations as a memory source. This work has been published in
ICCV 2023 [33], cf. fig. 5.b.

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Figure 5: a. Overview of the MultiSoc architecture. For the agent of interest (surrounded by dotted
line), the input is its intrinsic information and a graph limited to its FoV. Each node is composed of the
current and consecutive predicted positions of the observed entities, and by a label discriminating entities
following their nature. b. P. Marza’s PhD work : NN with two implicit representations as inductive biases
for autonomous multi-object navigation — both are learned online during each episode. (2) semantic
representation f s predicts positions x from goals g given as semantic codes. (3) structural representation
f o predicts occupancy and exploration s from positions x. (4) shows the reconstruction produced by a
decoder Dec during training. (1) is the ground-truth map.

8.4.4 Navigation Among Movable Obstacles: extension of NAMO to multi-robot and social constraints

Participants: Benoit Renault (PhD. stud.), Jacques Saraydaryan, Olivier Simonin,

David Brown (Ing. Inria).

In 2018 we started to study the NAMO planning problem (Navigation Among Movable Obstacles).

Since 2019, the PhD thesis of Benoit Renault aims to extend NAMO in two directions:

• Defining Social-NAMO, by considering where obstacles should be optimally moved with regards
to space access. We have defined a new spatial cost function based on social heuristics and the
topology of the environment. It allows to extended existing NAMO algorithms with the ability to
maintain area accesses (connectivity), see IROS publication [111].

• Defining Multi-robot NAMO (MR-NAMO). It consists in generalizing the problem to n robots that
can move the objects of the environment in order to reach their individual goal positions (see
Fig. 6.b). We have proposed a first coordination strategy based on local heuristics, able to detect
robots conflicts and to manage deadlock situations. If this approach is not complete we showed
experimentlay that it offers a significant gain for solving MR-NAMO problems compared to naive
coordination rules and standard NAMO algorithms. Publication in preparation.

During his PhD thesis, Benoit Renault developped a simulator for NAMO, and its extensions, called
S-NAMO-SIM (see Fig. 6.a). As this tools soon started to be used by the community, we asked for an Inria
ADT project. In 2023, we obtained this ADT (NAMOEX) which allowed to hire David Brown (Ing.), in order
to develop the simulator, by adding new functionnalisties, including its connexion with 3D simulators
and the monitoring of experiments with real robots.

On December, 19th, 2023, Benoit Renault defended his Phd (Navigation Among Movable Obstacles

(NAMO) Extended to Social and Multi-Robot Constraints), see [40].

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Inria Annual Report 2023

Figure 6: Illustration of a) S-NAMO planning b) MR-NAMO planning.

8.5 Swarm of UAVs: decentralized control, path-planning and communication

8.5.1 Multi-UAV Exploration and Visual Coverage of 3D Environments

Participants:

Alessandro Renzaglia, Olivier Simonin, Benjamin Sportich.

Multi-robot teams, especially when involving aerial vehicles (UAVs 19), are extremely efficient systems
to help humans in collecting information on large and complex environments. In these scenarios, cooper-
ative coverage and exploration are two fundamental problems with numerous important applications,
such as mapping, inspection of complex structures, and search and rescue missions. In both cases,
the robots’ goal is to navigate through the environment and cooperate to acquire new information to
accomplish the mission in the minimum possible time.

Following our recent results on these problems [114], [113], in the last year we focused on a strictly
related path planning problem where a fleet of UAVs must use both prior information and online gathered
data to efficiently inspect large surfaces such as ship hulls and water tanks [79]. This work is conducted
in the framework of the H2020 European project BugWright2, where one central use-case is focused on
corrosion detection and inspection on cargo ships. In our case, UAVs can detect corrosion patches and
other defects on the surface from low-resolution images. If defects are detected, they get closer to the
surface for a high-resolution inspection. The prior information provides expected defects locations and
is affected by both false positives and false negatives. The mission objective is to prioritize the close-up
inspection of defected areas while keeping a reasonable time for the coverage of the entire surface. In this
work, we propose two solutions to this problem: a coverage algorithm that divides the problem into a set
of Traveling Salesman Problems and a cooperative frontier approach that introduces frontier utilities to
incorporate the prior information.

A new ANR project (ANR JCJC project "AVENUE"), started in January 2023, aims now to further
investigate these research lines by focusing on the cooperative exploration and 3D reconstruction of
complex unknown environments with a fleet of aerial vehicles. Within this framework, a new PhD student,
Benjamin Sportich, began his thesis in November 2023, co-supervised by Alessandro Renzaglia and Olivier
Simonin.

8.5.2 Decentralized control of swarm of UAVs: Flocking models

Participants:

Alexandre Bonnefond (Phd. Inria/CITI), , Isabelle Guerrin-Lassous
(Lyon 1/LIP), , Olivier Simonin, , Johan Faure.

19Unmanned Aerial Vehicles

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This work was mainly conduced through the Inria/DGA "AI" project "DYNAFLOCK" (2019-23). It

funded the PhD thesis of Alexandre Bonnefond.

In this project, we studied communication-based flocking models (decentralized swarm navigation)
and proposed extensions to improve their abilities to deal with environments having obstacles. Often
depicted as robust systems, there is yet a lack of understanding how flocking models compare and how
they are impacted by the communication quality when they exchange control data.

After extending and combining two standard models, Olfati-Saber [100] and Vásárhelyi et al. [127], we
showed their limits (see IROS 2021[59] and Fig. 7.a). In 2022/2023, we proposed a new model, called APR
(Asymetric Pressure Regulation) [39]. This model introduces an individual measure of pressure allowing
to define a new flocking control able to anticipate dangerous/close agents interactions. Alexandre
Bonnefond defended his PhD thesis on June 14th, 2023.

In 2023, we also continued on developing flocking simulations with the Gazebo 3D simulator (mainly
supported by Johan Faure). We also conducted real experiments with PX4 Vision UAVs, embedding
UWB-based distance measurement, and allowing to test flocking/formation experiments (see Fig. 7.b).

8.5.3 Communication and localization in swarm of UAVs

Participants: Olivier Simonin, Thierry Arrabal, Théotime Balaguer (PhD student),
Stephane d’Alu(CITI, INSA Lyon) , Johan Faure, Isabelle Guerin-
Lassous (Lyon 1/Lip), Nicolas Valle, Hervé Rivano (Agora team)
.

In 2023, we continued the work within the CONCERTO ANR project : study of efficient communica-
tions in fleet of UAVs (aerial robots). With the arrival of Thierry Arrabal (post-doc/engineer), we built up
an experimental platform that aim to be as "plug-n-play" as possible. The first step was the deployment of
a network infrastructure to connect any needed device (computer, UAV, camera, etc..). Then, UAVs have
been configured to allow them to fly both indoor and outdoor (in aviaries). An experimental pipeline have
been develop (and still under active improvement) to facilitate the conduction of regular experimental
studies.

This platform is used to study the limitations of the networking capacity in UAV communications and
how these limitations mights affect their control. For instance, we conducted batches of experiments
consisting in sending ROS2 messages to pilot UAVs throught a Wi-Fi network while actively perturbing
the Wi-Fi channel. Some of these tests have been done in association with the ARMEN team from the
LS2N in Nantes to benefits from their MOCAP system to get precise data about UAV poses. In figure 8,
we compare the UAV behavior without network perturbation (in blue) against the UAV behavior with
network perturbation (in red) while a remote computer sends periodical command to perform a rotation
on the Z-axis (carousel behavior).

In 2023, we also continued to study the Ultra-Wide Band (UWB) technology to estimate distance
between UAVs. In particular, in the paper [25], we explored the possibility to embed several UWB

Figure 7: (a) Our flocking simulator (based on the Viragh’s platform) (b) The outdoor inflatable aviary.

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Inria Annual Report 2023

Figure 8: Network perturbation on UAV remote control for carousel behavior : blue is yaw measures
without perturbation, red is yaw measures with perturbation.

emitter/receiver per UAV in order to estimate distances and the relative angle.

In the following of our early work [67], we designed supports to embed UWB chips on our PX4 Vision
UAVs. In 2023, we conducted experiments with 3 PX4 UAVs, to test autonomous leader-follower formation
flights. This work is continuing by experimenting flocking models.

8.6 Multi-Agent Sequential Decision-Making under Uncertainty

This research is the follow up of a group led by Jilles S. Dibangoye carried out during the last seven years,
which include foundations of sequential decision making by a group of cooperative or competitive robots
or more generally artificial agents. To this end, we explore combinatorial, convex optimization and
reinforcement learning methods.

Dynamic programming and heuristic search are at the core of state-of-the-art solvers for sequential
decision-making problems. In partially observable or collaborative settings (e.g., POMDPs and Dec-
POMDPs), this requires introducing an appropriate statistic that induces a fully observable problem as
well as bounding (convex) approximators of the optimal value function. This approach has succeeded
in some subclasses of 2-player zero-sum partially observable stochastic games (zs-POSGs) as well, but
failed in the general case despite known concavity and convexity properties, which only led to heuristic
algorithms with poor convergence guarantees. We overcome this issue, leveraging on these properties to
derive bounding approximators and efficient update and selection operators, before deriving a prototyp-
ical solver inspired by HSVI that provably converges to an ϵ-optimal solution in finite time, and which we
empirically evaluate. This opens the door to a novel family of promising approaches complementing
those relying on linear programming or iterative methods [14].

8.6.1 Global Min-Max Computation for alpha-Hölder Zero-Sum Games

Participants:

Aurelien Delage (PhD Student), Jilles S. Dibangoye, , Olivier Buffet (In-
ria Nancy, Larsen).

Min-max optimization problems recently arose in various settings. From Generative Adversarial
Networks (GANs) to aerodynamic optimization through Game Theory, the assumptions on the objective
function vary. Motivated by the applications to deep learning and especially GANs, most recent works
assume differentiability to design local search algorithms such as Gradient Descend Ascent (GDA). In
contrast, this work will only require α-Hölder properties to tackle general game-theoretic problems with
poor continuity as- sumptions. Focusing on the example of problems in which max and min optimization
variables live in simplices, we provide a simple algorithm, based on Deterministic Optimistic Optimization
(DOO), relying on an outer min-optimization using the solutions of an inner max-optimization. The
algorithm is shown to converge in finite time to an ϵ-global optimum. Experimental validations are given
and the time complexity of our algorithm is studied. This work led to a publication [26].

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8.6.2 Optimally Solving Decentralized POMDPs Under Hierarchical Information Sharing

Participants:

Johan Peralez (Post-doc), Aurélien Delage (PhD Student), Olivier Buf-
fet (Inria Nancy, Larsen), Jilles S. Dibangoye.

This work is the result of an effort to provide a strong theory for optimally solving decentralized
decision-making problems, represented as decentralized partially observable Markov decision pro-
cesses (Dec-POMDPs), with tractable structures [128]. This work describes core ideas of multi-agent
reinforcement learning algorithms for decentralized partially observable Markov decision processes
(Dec-POMDPs) under hierarchical information sharing. That is the dominant management style in our
society—each agent knows what her immediate subordinate knows. In this case, we prove backups to be
linear due to (normal-form) Bellman optimality equations being for the first time specified in extensive
form. Doing so, one can sequence agent decisions at every point in time while still preserving optimality;
hence paving the way for the first optimal Q-learning algorithm with linear-time updates for an important
subclass of Dec-POMDPs. That is a significant improvement over the double-exponential time required
for normal-form Bellman optimality equations, in which updates of agent decisions are performed in sync
every point in time. Experiments show algorithms utilizing extensive instead of normal-form Bellman
optimality equations can be applied effectively to much larger problems. This work is under review for a
publication in an international conference.

8.6.3 Optimally Solving Decentralized POMDPs: The Sequential Central Learning Approach

Participants:

Johan Peralez (Post-doc), Aurélien Delage (PhD Student), Olivier Buf-
fet (Inria Nancy, Larsen), Jilles S. Dibangoye.

The centralized learning for simultaneous decentralized execution paradigm emerged as the state-of-
the-art approach to ϵ-optimally solving a simultaneous- move decentralized partially observable Markov
decision process, but the scalability remains a significant issue. This paper presents a novel—yet more
scalable—alternative, namely sequential central learning for simultaneous decentralized execution. This
methodology pushes the applicability of Bellman’s principle of optimality further, raising three new
properties. First, it allows a central learner to reason upon sufficient sequential statistics instead of
prior simultaneous ones. Next, it proves that ϵ-optimal value functions are piecewise linear and convex
in sufficient sequential statistics. Finally, it drops the time complexity of the backup operators from
double exponential to linear at the expense of longer planning horizons. Besides, it makes it easy to use
single-agent methods—e.g., a reinforcement learning algorithm enhanced with these findings applies
while still preserving its asymptotic convergence guarantees. Experiments on standard 2- as well as
n-agent benchmarks from the literature against state-of-the-art ϵ-optimal solvers confirm the superiority
of the novel paradigm.

8.6.4 Learning To Act Optimally in Sequential-Move Dec-POMDPs: Quantized Q-learning Algorithms

Participants:

Jacopo Castellini (Post-doc), Johan Peralez (Post-doc), Jilles S. Diban-
goye.

Policy optimization is currently the dominant approach to solve sequential-move n-agent Dec-
POMDPs. Methods of this family demonstrated an impressive ability to scale up on large-scale entertain-
ment applications, yet they often fail to provide provable guarantees in the face of critical applications
with significant consequences on humans or wildlife, e.g. search and rescue, wildlife protection or
autonomous vehicles. This work introduces core ideas for the design of optimal multi-agent value-based
reinforcement learning algorithms for sequential-move n-agent Dec-POMDPs. It leverages the piece-wise
linearity and convexity (PWLC) property of the optimal Q-value functions in sequential-move n-agent

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Inria Annual Report 2023

Dec-POMDPs. In particular, it introduces a temporal-different update rule that ensures asymptotic con-
vergence toward an optimal solution with a linear-time complexity. The paper also describes quantized
Q-learning algorithms, featuring a tabular representation of PWLC Q-value functions. These algorithms
succeed where state-of-the-art policy- and value-based methods fail. They eventually find optimal
solutions in small to medium-sized domains and scale up to much larger problems from the literature.
This algorithmic scheme also lays the foundation for further work on tabular and neural value-based
multi-agent reinforcement learning methods.

8.6.5 Stable Reinforcement Learning Methods

Participants:

Jilles S. Dibangoye, , Vincent Andrieu (LAGEPP, Lyon), Daniele As-
tolfi (LAGEPP,
Lyon),
Lyon), Giacomo Casadei
Madiha Nadri (LAGEPP, Lyon), Samuele Zoboli (PhD Student,
LAGEPP, Citi lab).

(LAGEPP,

This recent research project is at the intersection of three closely related research fields: optimal
control, machine learning, and mobile robotics. In this project, we try to find the best compromise
between control methodologies to provide stability guarantees and machine learning methodologies that
can scale up and converge towards a locally optimal solution. Applications of this hybrid methodology
concern mobile robotics, including the driving of drones at very high speed. Two PhD. theses have been
started on this problem the last year.

PhD thesis of Samuele Zoboli (since Jul. 2020, ANR DELICIO). In control theory, a natural approach
for the design of stabilizing control is to consider a linear approximation around the operating point of the
dynamic system. In this way, it is possible to design control laws that are optimal and which guaranteed
stability for the linearized system. However, this behavior is only guaranteed for the original system
around this operating point (local approach). The PhD thesis of S. Zoboli developped new real time
algorithms (contol law and estimation algorithms for dynamical systems) that combines tools coming
from control theory and reinforcement learning. He defended his PhD thesis on 28/09/2023.

8.6.6 Event-based Neural Learning for Quadrotor Control

Participants:

Estéban Carvalho (PhD Student, GIPSA lab, Citi lab), Pierre Susbi-
elle (GIPSA lab, Grenoble), Ahmad Hably (GIPSA lab, Grenoble), Nic-
olas Marchand (GIPSA lab, Grenoble), Jilles S. Dibangoye.

The design of a simple and adaptive flight controller is a real challenge in aerial robotics. A simple
flight controller often generates a poor flight tracking performance. Furthermore, adaptive or learning
algorithms are costly in time and resources, and may cause instability problems, for instance in the
presence of disturbances. In this paper, we propose an event-based neural learning strategy that combines
the use of a standard cascaded flight controller enhanced using a deep neural network that learns the
disturbances to improve the tracking performance. The strategy relies on two events: one allowing
the improvement of tracking errors and a second to ensure the stability of the closed-loop system.
After a validation of the proposed strategy in a ROS/Gazebo simulation environment, its effectiveness is
confirmed on real experiments, in the presence of wind disturbance. In 2023, this work has been published
in Autonomous Robots journal [13], and Esteban Carvalho defended his Phd thesis on 20/04/2023.

8.7 Constrained Optimisation problems

8.7.1 Constraint Programming

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33

Participants:

Jean-Yves Courtonne (Inria STEEP), François Delobel (LIMOS),
Patrick Derbez (Inria Capsule), Léon Fauste, Arthur Gontier (Inria Cap-
sule), Mathieu Mangeot (Inria STEEP), Loïc Rouquette, Christine Sol-
non.

In many of our applications, we have to solve constrained optimization problems (COPs) such as
vehicle routing problems, or multi-agent path finding problems, for example. These COPs are usually
NP-hard, and we study and design algorithms for solving these problems. We more particularly study
approaches based on Constraint Programming (CP), that provides declarative languages for describing
these problems by means of constraints, and generic algorithms for solving them by means of constraint
propagation. In [34], we introduce canonical codes for describing benzenoids, and we introduce CP
models for efficiently generating all benzenoids that satisfy some given constraints.

Post-doc of Loïc Rouquette (Dec. 2022 - Jul. 2023). This post-doc was funded by the ANR project
DeCrypt, that aims at designing declarative languages (based on CP) for solving cryptanalysis problems,
and it is in the continuation of the PhD thesis of Loïc. During his PhD thesis, Loïc has designed new
propagation algorithms that are integrated in the Choco CP library [117], new CP models for mounting
related-key differential attacks of Rijndael [116] and Boomerang attacks on Feistel ciphers [86]. During
his post-doc, he has integrated these algorithms in Tagada, a tool for automatically generating CP models
for mounting related-key differential attacks given a graph-based specification of the cipher [27]

PhD thesis of Léon Fauste (since Sept. 2021). This thesis is funded by Ecole Normale Supérieure Paris
Saclay and it is co-supervised by Mathieu Mangeot, Jean-Yves Courtonne (from Inria STEEP team) and
Christine Solnon. The goal of the thesis is to design new tools for choosing the best geographical scale
when relocating productive activities, and the context of this work is described in [66]. During the first
months of his thesis, Léon has designed a first Integer Linear Programming (ILP) model described in [76].
In 2023/2024, Léon is on leave (césure) for one year in order to follow a Master in Geography.

8.7.2 Multi-Robot Path Planning with cable constraints

Participants:

Xiao Peng (PhD. student CITI), Christine Solnon, Olivier Simonin.

The research is done within the context of the european project BugWright2 that aims at designing

an adaptable autonomous multi-robot solution for servicing ship outer hulls.

PhD thesis of Xiao Peng (since Nov. 2020). The goal of this PhD thesis is to design algorithms for
planning a set of mobile robots, each attached to a flexible cable, to cover an area with obstacles. In [106],
we formally define the Non-Crossing MAPF (NC-MAPF) problem and show how to compute lower and
upper bounds for this problem by solving well known assignment problems, we introduce a Variable
Neighbourhood Search approach for improving the upper bound, and we introduce a CP model for
solving the problem to optimality. In [18], we extend this approach to the case of non point-sized robots.
In 2023, we also extended the approach to deal with the coverage of a whole area using a single tethered
robot, then we examined different algotihms to divide the environment in order to distribute the coverage
to several robots.

8.7.3 Vehicle routing problems

Participants:

Romain Fontaine (PhD Student CITI), Jilles S. Dibangoye, Christine Sol-
non.

The research is done within the context of the transportation challenge and funded by INSA Lyon.

Integration of traffic conditions:
In classical vehicle routing problems, travel times between locations
to visit are assumed to be constant. This is not realistic because traffic conditions are not constant

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Inria Annual Report 2023

throughout the day, especially in an urban context. As a consequence, quickest paths (i.e., successions of
road links), and travel times between locations may change along the day. To fill this lack of realism, cost
functions that define travel times must become time-dependent. These time-dependent cost functions
are defined by exploiting data coming from sensors, and the frequency of the measures as well as the
number and the position of the sensors may have an impact on tour quality.

PhD thesis of Romain Fontaine (since Oct. 2020). State-of-the-art approaches for solving vehicle
routing problems hardly scale when considering time-dependent cost functions. The goal of the PhD
thesis of Romain is to design new algorithms based on dynamic programming. Constraints (such as,
for example, time-windows or pickup-and-delivery constraints) are exploited to prune states, and new
bounds and heuristics are designed to guide the search. The approach is described in [15].

9 Bilateral contracts and grants with industry

9.1 Bilateral contracts with industry

9.1.1 Toyota Motor Europe (2006 - 2023)

Participants: Christian Laugier, David Sierra González, Özgür Erkent, Jilles Diban-

goye, Christian Wolf, Wenqian Liu, Gustavo Salazar-Gomez.

The contract with Toyota Motors Europe is a joint collaboration involving Toyota Motors Europe, Inria
and ProbaYes. It follows a first successful short term collaboration with Toyota in 2005. This contract aims
at developing innovative technologies in the context of automotive safety. The idea is to improve road
safety in driving situations by equipping vehicles with the technology to model on the fly the dynamic
environment, to sense and identify potentially dangerous traffic participants or road obstacles, and
to evaluate the collision risk. The sensing is performed using sensors commonly used in automotive
applications such as cameras and lidar.

This collaboration has been extended in 2018 for 4 years (period 2018-2021) and Toyota provides
us with an experimental vehicle Lexus equipped with various sensing and control capabilities. Several
additional connected technical contracts have also been signed, and an exploitation license for the
CMCDOT software has been bought by Toyota in 2018.

9.1.2 Sumitomo Electric Industries (2020-23)

Participants:

Lukas Rummelhard, Robin Baruffa, Hugo Bantignies, Nicolas Turro,
Christian Laugier.

R&D project to develop and demonstrate multi-modal perception using communicating infrastruc-
tures, data exchange and fusion between such infrastructure and mobile autonomous vehicles, and
the added value of such architecture. Based on embedded Bayesian perception systems (CMCDOT
framework), the project aims to develop long-term prediction and risk assessment.

9.2 Bilateral grants with industry

9.2.1 IRT Nanoelec PULSE – Security of Autonomous Mobile Robotics (2023-2025)

Participants:

Lukas Rummelhard, Thomas Genevois, Robin Baruffa, Hugo Bantig-
nies, Nicolas Turro, Christian Laugier
.

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35

Chroma participates in a project supported by PIA in the scope of the program PULSE of IRT Nanoelec.
The objective of this project is to integrate, develop and promote technological bricks of context capture,
for the safety of the autonomous vehicle. Building on Embedded Bayesian Perception for Dynamic
Environment, Bayesian data fusion and filtering technologies from sets of heterogeneous sensors, these
bricks make it possible to secure the movements of vehicles, but also provide them with an enriched
and useful representation for autonomy functions themselves. In this context, various demonstrators
embedding those technology bricks are developed in cooperation with industrial partners. Current
work is mainly focused on distributed perception using realistic communication systems and Bayesian
occupancy grids, and integration of Neural-Network-based systems into probabilistic frameworks.

9.2.2 MeanWhile (2021 - 2023)

Participants:

Fabrice Jumel, , Jacques Saraydaryan.

This contract is a BPI France funding between MeanWhile compagny and CPE Lyon. The project concerns
the development of an indoor geolocation device for third-party equipment for a fleet of autonomous
mobile robots. It funds an engineer position (18 months CPE/Meanwhile).

10 Partnerships and cooperations

10.1 International research visitors

10.1.1 Visits of international scientists

Name of the researcher François Michaud

Status (researcher) Full Professor

Institution of origin: Univ. of Sherbrooke, 3IT Institute, IntRoLab

Country: Canada

Dates: 20/5/2023 - 2/06/2023

Context of the visit: Building a collaboration between IntRoLab team (Can.) and Chroma team.

Mobility program/type of mobility: research stay

10.2 European initiatives

10.2.1 H2020 projects

BugWright2 - H2020 ICT (Autonomous Robotic Inspection and Maintenance on Ship Hulls and Storage
Tanks)

• 650€ for Chroma and Agora teams - total 10 M€ — 2020-24

• The project deals with the inspection of large infrastructures conducted by multiple heterogeneous
robots (aerial, ground and underwater). Chroma coordinates the Work Package 6 on multi-robot
planning, led by O. Simonin.

• Consortium (academic partners only): CNRS/GeorgiaTech Metz (coord), INSA-Inria Lyon 5chroma
& Agora teams), LakeSide lab. (Austria), Universität Klagenfurt, Universitat Trier, Universidade de
Porto, Universitat de Les Illes Balears.

• www.bugwright2.eu/

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Inria Annual Report 2023

10.3 National initiatives

10.3.1 ANR

ANR TSIA "VORTEX" project Vision-based Reconfigurable Drone Swarms for Fast Exploration

Participants: Olivier Simonin, Alessandro Renzaglia, Nicolas Valle.

Partners : CITI/INSA Lyon Chroma, LS2N (Nantes), ENSTA (Paris), ONERA (Paris), LabSTICC/IMT

(Brest). Coord. : O. Simonin (Chroma) budget : 694 K€

The VORTEX project proposes a new approach for exploring unknown indoor environments using
a fleet of autonomous drones (UAVs). We propose to define a strategy based on swarm intelligence
exploiting only vision-based behaviors. The fleet will deploy as a dynamic graph self reconfiguring
according to events and discovered areas. Without requiring any mapping or wireless communication,
the drones will coordinate by mutual perception and communicate by visual signs. This approach will be
developed with RGB and event cameras to achieve fast and low-energy navigation. Performance, swarm
properties, and robustness will be evaluated by building a demonstrator extending a quadrotor prototype
developed in the consortium.

ANR Challenge MOBILEX : ’NAVIR’ project

Participants:

Lukas Rummelhard, Robin Baruffa, Hugo Bantignies, Nicolas Turro,
Christian Laugier.

In order to accelerate research and innovation on autonomous mobility of land vehicles in complex
environments (damaged roads, presence of obstacles, etc.), the French National Research Agency (ANR)
and the Defense Innovation Agency (AID) launch a co-funded call: the MOBILEX Challenge (MOBILity in
Complicated Environments), in partnership with the French National Center for Space Studies (CNES)
and the French Agency for Innovation in Transport (AIT).

The development of advanced piloting assistance, on board or remote in the form of an autopilot, to
manage the trajectory of a land vehicle taking into account the complexity of its environment (slopes,
unstructured roads, landslides, etc.), and its evolution (meteorological hazards), represents a crucial
research challenge for the civil, military and space fields. Having such a level of assistance would not
only relieve the pilots, but also free up cognitive and/or physical resources, whether for construction,
fire-fighting or military vehicles for example. In this context, the MOBILEX Challenge aims at evaluating
different technological solutions integrating all the functions and constraints to be taken into account to
manage the local trajectory of a land vehicle in an autonomous way in a complex environment. More
globally, it aims to advance research and innovation in this field.

ANR MAMUT (2023-2026)
tion)

(Machine learning And Metaheuristics algorithms for Urban Transporta-

Participants: Christine Solnon, Jilles Dibangoye, Olivier Simonin.

Consortium: Lab-STICC (coord.), Chroma/CITI, MapoTempo. The main objective of MAMUT is to
develop new algorithms for optimizing urban delivery tours. Total funding 522 K€.

Project CHROMA

37

ANR JCJC "AVENUE" (2023-2026)
Unknown Environments)

(Cooperative Aerial Vehicles for Non-Uniform Mapping of 3D

Participants:

Alessandro Renzaglia.

The objective of AVENUE is to propose new efficient solutions to generate online trajectories for a team
of cooperating aerial vehicles to achieve complete exploration and non-uniform mapping of an unknown
3D environment. The main challenges will be the definition of new criteria to predict the expected
information gain brought by future candidate observation points and the design of a distributed strategy
to optimally assign future viewpoints to the robots to finally minimize the total mission time. Total
funding 250K€.

ANR "MaestrIoT" (2021-2025)

(Multi-Agent Trust Decision Process for the Internet of Things)

Participants:

Jilles Dibangoye, Olivier Simonin.

Consortium: LITIS (coord.), Chroma/CITI, LCIS, IHF/LIMOS. The main objective of the MaestrIoT project
is to develop an algorithmic framework for ensuring trust in a multi-agent system handling sensors and
actuators of a cyber-physical environment. Trust management has to be ensured from the perception to
decision making and integrating the exchange of information between WoT devices. Total funding 536
K€.

ANR "CONCERTO" (2020-23)

Participants: Olivier Simonin, Thierry Arrabal.

Title: "mobilité Contrôlée et cOmmunicatioNs effiCaces dans une flottE de dRones auTo-Organisée".
The project is led by Isabelle Guerin-Lassous (LIP/Inria Dante). Partners: LIP (UCBL/Inria Dante), CITI
(INSA/Inria Chroma), LS2N (CNRS, Nantes), LORIA (UL, Nancy), Alerion (company in Nancy). Funding:
300K€ - (ASTRID ANR).

ANR "Annapolis" (2022-2026)

Participants:

Anne Spalanzani, Kaushik Bhowmik.

Title: "AutoNomous Navigation Among Personal mObiLity devIceS". In ANNAPOLIS, unforeseen and
unexpected events take source from situations where the perception space is hidden by bulky road
obstacles (e.g. truck/bus) or an occluding environment, from the presence of new powered personal
mobility platforms, or from erratic behaviors of unstable pedestrians using (or not) new electrical mobility
systems and respecting (or not) the traffic rules ANNAPOLIS will increase the vehicle’s perception capacity
both in terms of precision, measurement field of view and information semantics, through vehicle to
intelligent infrastructure communication. Total funding 831 K€.

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Inria Annual Report 2023

EquipEx+ "TIRREX" (2021-30)

Participants: Olivier Simonin, Alessandro Renzaglia, Christian Laugier, Lukas Rum-

melhard.

Title : "Technological Infrastructure for Robotics Research of Excellence" (∼ 12M€). TIRREX is led by
CNRS and involves 32 laboratories. Chroma is involved in two research axes :

• "Aerial Robotics", Chroma/CITI lab. is a major partner of this axis (INSA Lyon & Inria). We can

benefit from the experimental platforms (in Grenoble and Marseille) and their support.

• "Autonomous land robotics", Chroma/CITI lab. is a secondary partner of this axis (INSA Lyon &

Inria).

AI Chair "REMEMBER" (2020-23) (Chair of research and teaching in artificial intelligence)

Participants: Christian Wolf (NaverLabs europe, LIRIS/INSA Lyon), Olivier Simonin,

Jilles Dibangoye.

Title : Learning Reasoning, Memory and Behavior (funding 574K€). This Chair is led by Christian Wolf
(INSA Lyon/LIRIS lab.), who recently moved to NaverLabs Europe. It involves CITI/Chroma (O. Simonin,
J. Dibangoye) and LIRIS/SyCoSMA (L. Matignon). The chair is co-financed by ANR, Naver Labs Europe
and INSA-Lyon. Creating agents capable of high-level reasoning based on structured memory is the main
topic of the AI Chair "REMEMBER". Funding one PhD in Chroma (P. Marza).

ANR JCJC "Plasma" (2019-2023)

Participants:

Jilles Dibangoye.

The ANR JCJC Plasma, led by Jilles S. Dibangoye, aims at developing a general theory and algorithms
with provable guarantees to treat planning and (deep) RL problems arising from the study of multi-
agent sequential decision-making, which may be described as Partially Observable Stochastic Games
(POSG), see Figure 1. We shall contribute to the development of theoretical foundations of the fields
of intelligent agents and MASs by characterizing the underlying structure of the multi-agent decision-
making problems and designing scalable and error-bounded algorithms. The research group is made of
three senior researchers, O. Simonin, F. Charpillet (INRIA Nancy) and O. Buffet (INRIA Nancy), and two
junior researchers Jilles S. Dibangoye and A. Saffidine (Univeristy of New South Whales). We received a
support for 42-months starting in March 2020 with a financial support of about 254K euros (one PhD and
one post-doc are in progress).

ANR "Delicio" (2019-2023)

Participants:

Jilles Dibangoye, Olivier Simonin.

Project CHROMA

39

The ANR Delicio (Data and Prior, Machine Learning and Control), led by C. Wolf (NaverLabs Europe/INSA
Lyon), proposes fundamental and applied research in the areas of Machine Learning and Control with
applications to drone (UAV) fleet control. The consortium is made of 3 academic partners: INSA-
Lyon/LIRIS, INSA-Lyon/CITI (Chroma team), University Lyon 1/LAGEPP, and ONERA. We received a
support for 48-months starting in October 2019 with a financial support of about 540K euros.

10.3.2 PEPR

PEPR Agroécologie et numérique: NINSAR project
Robots, 60 months, since 1/1/2023).

(New ItiNerarieS for Agroecology using cooperative

Participants: Olivier Simonin, Alessandro Renzaglia.

Chroma is partner of the NINSAR project. The project will fund a PhD thesis on heterogenous ground
& aerial robot cooperation and path-planning. Olivier Simonin coordinates our participation (168 K€ for
Chroma partner, the total funding is 2,16 M€)

PEPR O2R Organic Robotics

(96 months, since 1/1/2023)

Participants: Olivier

Simonin,

Anne

Spalanzani,

Alessandro Renzaglia,

Jacques Saraydaryan.

Chroma is involved in the O2R’s project "Interactive Mobile Manipulation" (led by Andrea Cherubini

from Université de Montpellier/LIRMM). Budget 3.5 M€.

Main topics adressed by Chroma are Social navigation, Multi-robot planning & NAMO problems and

Perception of complex scenes with humans.

10.3.3 Grand Defis

Grand Défi "PRISSMA" (2021-2024)

20

Participants:

Alessandro Renzaglia, Christian Laugier, Jean-Baptiste Horel, Pier-
rick Koch, Khushdeep Mann.

The project is funding 330K€ for Chroma (6.5M€ total). The PRISSMA consortium is composed of 21
French members from both industry and academia, including: UTAC (coord.), Inria (Chroma, Convecs),
LNE, UGE, CEA, Navya, IRT SystemX, and others. This project aims at proposing a platform that will allow
to lift the technological barriers preventing the deployment of secure AI-based systems, and to integrate
all the elements necessary for the realization of the homologation activities of the autonomous vehicle
and its validation in its environment for a given use case.

10.3.4 DGA/INRIA AI projects

"BioSwarm" (2023-2027)

Participants:

Emanuele Natale (Inria Coati, Sophia Antipolis), Olivier Simonin.

20Grand Défi du Conseil de l’Innovation et le Ministère de la Transition Ecologique et Solidaire

40

Inria Annual Report 2023

Co-lead by E. Natale (Inria Coati) and O. Simonin (Chroma).
The BioSwarm project concerns the application of decentralised algorithms, originally motivated by
the modelling the collective behaviour of biological systems, to swarms of drones dealing with a collective
task. We are focusing on search strategies in unknown environments and on collective decision-making
by consensus. Focusing on the context of defence-related applications, we study these topics by particular
attention to the robustness of the proposed solutions (e.g. to adversary attacks or sabotage by certain
agents), and we are aiming for solutions that, while adaptable to large swarms, are already effective for
small fleets of just half a dozen drones., aims to [TO COMPLETE]
Funding of BioSwarm : 253 K€

"DYNAFLOCK" (2019-2023)

Participants:

Alexandre Bonnefond, Olivier Simonin, Isabelle Guerin-Lassous
(Lip/Lyon 1, Inria Dante), Johan Faure.

The DYNAFLOCK project, led by O. Simonin, aims to extend flocking-based decentralized control of
swarm of UAVs by considering the link quality between communicating entities. The consortium is made
of 2 Inria teams from Lyon : Chroma and Dante (involving Prof. I. Guerin-Lassous). The PhD student
(Alexandre Bonnefond) recruited in this project aims at defining dynamic flocking models based on the
link quality ([59]). In 2022, Johan Faure has been recruited as engineer to develop simulations and to
conduct experiments with a quadrotors platform.
Funding of Dynaflock : 250 K€

10.4 Regional initiatives

R&D Booster "Moov-IT" project (2020 – 2023)

Participants:

Thomas Genevois, Lukas Rummelhard, Christian Laugier.

The objective of the project is to develop an autonomous system to be integrated in production chains
to provide modernization, flexibility and productivity, including fixed and mobile transitic systems. In
that perspective, Chroma’s CMCDOT framework will be adapted, extended and integrated on a mobile
robot from Akeoplus, and tested in real environments.

11 Dissemination

Participants:

all members .

11.1 Promoting scientific activities

11.1.1 Scientific events: organisation

Member of the organizing committees

• Fabrice Jumel was co-local chair Robocup@home Bordeaux 2023.

Project CHROMA

41

11.1.2 Scientific events: selection

Chair of conference program committees

• Christian Laugier is Editor-in-Chief of the IROS Conference Paper Review Board (CPRB) for the

period 2023-25 (IEEE/RSJ International Conference on Intelligent Robots and Systems).

Chair of conference award committees

• Christian Laugier was chair of the "Outstanding Associate Editor Award" and of the "Outstanding
Reviewer Award" of the 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems
(IROS 2023).

Member of conference program committees

• Olivier Simonin was Senior Editor for IROS 2023 (IEEE/RSJ International Conference on Intelligent

Robots and Systems).

• Anne Spalanzani was associate editor of the IROS 2023 conference.

• Anne Spalanzani was member of the program committee of the ROBOT23 conference.

• Alessandro Renzaglia was Associate Editor for IROS 2023 (IEEE/RSJ International Conference on

Intelligent Robots and Systems).

Member of conference award committees

• Alessandro Renzaglia was member of the "Outstanding Associate Editor Award" and of the "Out-
standing Reviewer Award" of the 2023 IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS 2023).

Reviewer

• O. Simonin, A. Martinelli, C. Laugier, A. Spalanzani and A. Renzaglia serve each year as reviewer in

conferences IEEE IROS and IEEE ICRA.

• O. Simonin served as reviewer in conference IJCAI 2023.

11.1.3 Journal

Member of the editorial boards

• Christine Solnon is associate editor of Annals of Mathematics and Artificial Intelligence (AMAI),

and member of the editorial boards of Swarm Intelligence, and Constraints.

• Olivier Simonin is a member of the editorial board of the french revue of AI since 2018, named RIA

till 2020 and ROIA now.

• C. Laugier is member of the Editorial Board of the journal IEEE Transactions on Intelligent Vehicles

(Senior Editor). He is also member of the Steering Committee of the Journal.

• C. Laugier is member of the Editorial Board of the IEEE Journal Robomech.

• Christian Laugier is Editor-in-Chief of the IROS Conference Paper Review Board (CPRB) for the

period 2023-25 (IEEE/RSJ International Conference on Intelligent Robots and Systems).

Reviewer - reviewing activities

• O. Simonin was reviewer for RA-L Journal (IEEE Robotics and Automation Letter) and ROIA (Revue

Ouverte d’Intelligence Française).

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Inria Annual Report 2023

11.1.4 Leadership within the scientific community

• Christine Solnon is member of the "comité de direction" of the GDR IA (www.gdria.fr)

• Christine Solnon is member of the "Conseil d’Administration" of the TUBA (lyonurbandata.sfr-

monsitebusiness.fr)

• Anne Spalanzani is Co-animator of the GDR Robotique TS5 "Robotics and Data".

• Olivier Simonin is member of the Executive Committee of the PEPR O2R (Organic Robotics) 2023-

2030 (www.cnrs.fr/en/pepr/pepr-exploratoire-o2r-robotique).

• Olivier Simonin and Fabrice Jumel are members of Robocup France committee: (www.robocup.fr/le-

comite)

• Fabrice Jumel is a member of the organization committee of the Robocup@Home competition

(since 2016).

• Christian Laugier is a founding member and co-chair of the IEEE RAS Technical Committee on

"Autonomous Ground Vehicles and Intelligent Transportation Systems"

11.1.5 Scientific expertise

• Anne Spalanzani was a member of the jury of the “Georges Giralt Ph.D. Award” which showcases

PhD theses defended during 2023 in European Universities from all areas of robotics.

• Anne Spalanzani was a member of the ANR CE33 "robotics and interaction" committee in 2023.

11.1.6 Research administration

• O. Simonin is member of the Inria Lyon COS (comité d’orientation scientifique du Centre).

11.2 Teaching - Supervision - Juries

11.2.1 Supervision

Phd in progress :

• Benjamin Sportich, Next-Best-View Planning for 3D Reconstruction with Cooperative Multi-UAV

Systems, A. Renzaglia, O. Simonin (ANR AVENUE).

• Théotime Balaguer, Contrôle efficace de flotte de drones soumis à des contraintes de communic-
ation hétérogènes, O. Simonin, I. Guérin Lassous, I. Fantoni (Lyon1/LIP), (CNRS/LS2N Nantes)
(INSA/DGA).

• Rabbia Asghar, Real-time analysis of complex traffic situations by Predicting/Characterizing Abnor-

mal Behaviors and Dangerous Situations, A. Spalanzani, C. Laugier, L. Rummerhard.

• Manuel Diaz Zapata, AI-based Framework for Scene Understanding and Situation Awareness for

Autonomous Vehicles, J. Dibangoye, C. Laugier, O. Simonin.

• Jean-Baptiste Horel, Validation des composants de perception basés sur l’IA dans les véhicules
autonomes, R. Mateescu (Inria Convecs), A. Renzaglia, C. Laugier (funded by PRISSMA project).

• Pierre Marza, Apprentissage large-échelle d’agents autonomes capables de naviguer dans des
environnements réels, C. Wolf (NaverLabs Europe), L. Matignon (Lyon1/LIRIS), O. Simonin (funded
by Chaire IA "REMEMBER").

• Aurelien Delage, Zero-Sum Partially Observable Stochastic Games, Jilles Dibangoye and Olivier

Buffet (Larsen Inria Nancy), funded by ANR JCJC PLASMA.

Project CHROMA

43

• Romain Fontaine, Optimisation de la mobilité en ville en tenant compte des conditions de traffic, J.

Dibangoye and C. Solnon.

• Léon Fauste, Aide à la décision pour le choix de l’échelle lors de la relocalisation d’activités industri-
elles, M. Mangeot (STEEP, Inria Montbonnot), J.-Y. Courtonne (STEEP, Inria Montbonnot), and C.
Solnon.

• Xiao Peng, Planning Problems in a Multi-Tethered-Robot System, O. Simonin, C. Solnon, European

H2020 BugWright2 project funding. To be defended on 19/2/24.

• Thomas Genevois, Navigation de véhicule autonome à l’aide de grilles d’occupations probabilistes ».
Thèse en validation d’acquis, Université Grenoble Alpes. A. Spalanzani, C. Laugier. To be defended
in april 2024.

• Gustavo Salazar Gomez, AI-driven safe motion planning and driving decision-making for autonom-

ous driving. Thèse de l’Université Grenoble Alpes. A. Spalanzani, C. Laugier.

Phd defended in 2023 :

• Benoit Renault, NAvigation en milieu MOdifiable (NAMO) étendue à des contraintes sociales et

multi-robots, O. Simonin and J. Saraydaryan, defended on 19/12/23.

• Alexandre Bonnefond, Flocking models based on local communications : From theory to simula-

tions, O. Simonin and I. Guerrin-Lassous (Lyon 1), defended on 14/6/23

• Mihai Popescu, Static and Dynamic Multi-Robot Routing with Periodic Connectivity Maintenance,
Patrolling and Network Data Delivery, O. Simonin, A. Spalanzani, F. Valois (CITI/Inria Agora),
defended on 11/7/23.

• Zoboli Samuel, Learning globalization of locally stabilizable policies for nonlinear systems, Jilles
Dibangoye, Vincent Andrieu and Daniele Astolfi (LAGEPP,Lyon), funded by ANR PRC Delicio.
Defended on 28/09/23.

• Estéban Carvalho, Amélioration de l’efficacité du contrôle du vol des quadrirotors par apprentissage

neuronal, Ahmad Hably (Gipsa-Lab), Nicolas Marchand (Gipsa-Lab), Jilles S. Dibangoye.

11.2.2 Juries

• Anne was president of several PhD juries: Rotanna Ly (Université Grenoble Alpes, November 2023),

Charles-Olivier Artizzu (Université Côte d’Azur, June 2023)

• Anne was member of several PhD juries: Houssem Eddine Boulahbal (Université Côte d’Azur,
September 2023), Lina Mezghani (Université Grenoble Alpes, July 2023), Thomas Gilles (Ecole des
mines Paris, April 2023).

• Anne was examiner of Eric Lucet’s HDR, Sorbonne Université, october 2023.

• Olivier Simonin was president of the PhD thesis jury of Quentin Bruel (Centrale Lyon/CEA, 13/06/23)

and Jamy Chahal (LIP6/Sorbonne Univ., 30/11/23).

• Olivier Simonin was member, as reviewer, of the PhD thesis jury of Nicolas Fontbonne (Univ. Sor-
bonne, 14/03/23), Jad Bassil (FEMTO/U. Franche-Comté, 17/09/23), Félix Quinton (ISAE/ONERA
Toulouse, 1//11/23) and Aurélien Merci (Univ. Toulon, 15/12/23).

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Inria Annual Report 2023

11.2.3 Education

Olivier Simonin:

• INSA Lyon 5th year / Master - Robotics option (20 students) : AI for Robotics (plannning, RL,

bio-inspiration), Multi-Robot Systems, Robotics Projects, 80h, Resp., Telecom Dept.

• INSA Lyon 4th year / Master. : AI for telecom (Intro IA, Metaheuristics), 10h (90 students), Telecom

Dept.

• INSA Lyon 3rd year / Lic. : Algorithmics, 50h (90 students), L3, Resp., Telecom Dept.

Jilles S. Dibangoye :

• INSA Lyon 4th year / Master : Introduction to AI, 20h (90 students), Resp., Telecom Dept.

• INSA Lyon 3rd year / Lic. & Master : Operating Systems, 56h L3 and 16h Master, Telecom Dept.

Christine Solnon :

• INSA Lyon 3rd year / Lic. : Advanced algorithms for artificial intelligence and graphs, 40h (130

students), Resp., Computer Science Dept.

• INSA Lyon 4th year / Master : Object Oriented and Agile software development, 62h (130 students),

Resp., Computer Science Dept.

• INSA Lyon 5th year / Master : Fundamental Computer Science, 20h (60 students), Resp., Computer

Science Dept.

• INSA Lyon 5th year / Master : Prescriptive Data Analytics, 6h (30 students), Computer Science Dept.

Fabrice Jumel :

• CPE Lyon 4-5th year / Master : Robotic vision, cognitive science, HRI, deeplearning, robotic

platforms, Kalman Filter, 280h, SN Dept.

• Workshop Manager, Autonomous Mobile Robotics, "worldkills 2024", Lyon

Jacques Saraydaryan :

• CPE Lyon 5th year / Master : Autonomous Robot Navigation 24h, Particle Filter 12h, SN Dept.

• CPE Lyon 4-5th year / Master : Software Design and Big Data, 400h, Resp., SN Dept.

• CPE Lyon 4-5th year / Master : Software Architecture, 160h, SN Dept.

• CPE Lyon 4-5th year / Master : Introduction to Cyber Security, 20h, SN Dept.

Anne Spalanzani :

• INPG Ense3 Grenoble, Master MARS: Autonomous Robot Navigation 6h.

Agostino Martinelli:

• Master (M2R) MoSIG: Autonomous Robotics, 12h, ENSIMAG Grenoble.

Alessandro Renzaglia:

• INPG Ense3 Grenoble, Master MARS: AI and Autonomous Systems, 12h.

• INSA Lyon 5th year / Master - Robotics option: Multi-Robot Systems, 2h, Telecom Dept.

• INSA Lyon 3rd year / Lic.: supervision of research initiation projects (groups of 5 students) along

one semester, 15h, Telecom Dept.

Project CHROMA

11.2.4 Interventions

45

• Anne Spalanzani gave a conference to the "University Inter-âge" of Grenoble presenting recent

advances on autonomous cars

• F. Jumel, J. Saraydaryan and O. Simonin are members and founders of Lyontech Team (rob-

ocup@home) since 2017.

12 Scientific production

12.1 Major publications

[1] R. Asghar, L. Rummelhard, A. Spalanzani and C. Laugier. ‘Allo-centric Occupancy Grid Prediction
for Urban Traffic Scene Using Video Prediction Networks’. In: ICARCV 2022 - 17th International
Conference on Control, Automation, Robotics and Vision. Singapore, Singapore, 11th Dec. 2022.
URL: https://inria.hal.science/hal-03862095.

[2] E. Beeching, J. Dibangoye, O. Simonin and C. Wolf. ‘Learning to plan with uncertain topological
maps’. In: ECCV 2020 - 16th European Conference on Computer Vision. Glasgow, United Kingdom,
23rd Aug. 2020, pp. 1–24. DOI: 10.1007/978-3-030-58580-8_28. URL: https://inria.hal.s
cience/hal-02933641.

[3] G. Bono, J. Dibangoye, O. Simonin, L. Matignon and F. Pereyron. ‘Solving Multi-Agent Routing
Problems Using Deep Attention Mechanisms’. In: IEEE Transactions on Intelligent Transportation
Systems (28th July 2021), pp. 1–10. DOI: 10.1109/TITS.2020.3009289. URL: https://hal.arc
hives-ouvertes.fr/hal-02909132.

[4] Ö. Erkent, C. Wolf, C. Laugier, D. Sierra González and V. R. Cano. ‘Semantic Grid Estimation with
a Hybrid Bayesian and Deep Neural Network Approach’. In: IROS 2018 - IEEE/RSJ International
Conference on Intelligent Robots and Systems. Madrid, Spain: IEEE, Oct. 2018, pp. 1–8. URL: https:
//hal.inria.fr/hal-01881377.

[5] T. Genevois, J.-B. Horel, A. Renzaglia and C. Laugier. ‘Augmented Reality on LiDAR data: Going
beyond Vehicle-in-the-Loop for Automotive Software Validation’. In: IV 2022 - 33rd IEEE Intelligent
Vehicles Symposium IV. Aachen, Germany: IEEE, 5th June 2022, pp. 1–6. DOI: 10.1109/IV51971
.2022.9827351. URL: https://inria.hal.science/hal-03703227.

[6] M. Kabtoul, M. Prédhumeau, A. Spalanzani, J. Dugdale and P. Martinet. ‘How To Evaluate the
Navigation of Autonomous Vehicles Around Pedestrians?’ In: IEEE Transactions on Intelligent
Transportation Systems (23rd Oct. 2023), pp. 1–11. DOI: 10.1109/TITS.2023.3323662. URL:
https://inria.hal.science/hal-04255479.

[7] A. Martinelli. ‘Cooperative Visual-Inertial Odometry: Analysis of Singularities, Degeneracies and
Minimal Cases’. In: IEEE Robotics and Automation Letters 5.2 (2020), pp. 668–675. DOI: 10.1109
/LRA.2020.2965063. URL: https://hal.inria.fr/hal-02427991.

[8] A. Martinelli. ‘Nonlinear Unknown Input Observability: Extension of the Observability Rank
Condition’. In: IEEE Transactions on Automatic Control 64.1 (Jan. 2019), pp. 222–237. DOI: 10.110
9/TAC.2018.2798806. URL: https://hal.archives-ouvertes.fr/hal-01966303.

[9] X. Peng, O. Simonin and C. Solnon. ‘Non-Crossing Anonymous MAPF for Tethered Robots’. In:
Journal of Artificial Intelligence Research 78 (Oct. 2023), pp. 357–384. DOI: 10.1613/jair.1.143
51. URL: https://hal.science/hal-04216937.

[10] M. Prédhumeau, L. Mancheva, J. Dugdale and A. Spalanzani. ‘An Agent-Based Model to Predict
Pedestrians Trajectories with an Autonomous Vehicle in Shared Spaces’. In: Proceedings of the 20th
International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021). AAMAS
2021 - 20th International Conference on Autonomous Agents and Multiagent Systems. Online,
France, 3rd May 2021, pp. 1–9. URL: https://hal.archives-ouvertes.fr/hal-03113422.

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Inria Annual Report 2023

[11] A. Renzaglia, J. Dibangoye, V. Le Doze and O. Simonin. ‘A Common Optimization Framework for
Multi-Robot Exploration and Coverage in 3D Environments’. In: Journal of Intelligent and Robotic
Systems (2020). DOI: 10.1007/s10846-020-01255-4. URL: https://hal.inria.fr/hal-029
30144.

[12] L. Rummelhard, J.-A. David, A. Gonzalez Moreno and C. Laugier. ‘A cross-prediction, hidden-state-
augmented approach for Dynamic Occupancy Grid filtering’. In: ICARCV 2022 - 17th International
Conference on Control, Automation, Robotics and Vision. Singapore, Singapore, 11th Dec. 2022.
URL: https://inria.hal.science/hal-03974918.

12.2 Publications of the year

International journals

[13] E. Carvalho, P. Susbielle, N. Marchand, A. Hably and J. Dibangoye. ‘Event-based neural learning
for quadrotor control’. In: Autonomous Robots (23rd June 2023). DOI: 10.1007/s10514-023-101
15-7. URL: https://hal.science/hal-04140469.

[14] A. Delage, O. Buffet, J. Dibangoye and A. Saffidine. ‘HSVI Can Solve Zero-Sum Partially Observable
Stochastic Games’. In: Dynamic Games and Applications (2nd Sept. 2023), p. 58. DOI: 10.1007/s1
3235-023-00519-6. URL: https://inria.hal.science/hal-04382756.

[15] R. Fontaine, J. Dibangoye and C. Solnon. ‘Exact and Anytime Approach for Solving the Time
Dependent Traveling Salesman Problem with Time Windows’. In: European Journal of Operational
Research (2023). DOI: 10.1016/j.ejor.2023.06.001. URL: https://hal.science/hal-0412
5860.

[16]

J.-B. Horel, P. Ledent, L. Marsso, L. Muller, C. Laugier, R. Mateescu, A. Paigwar, A. Renzaglia and
W. Serwe. ‘Verifying Collision Risk Estimation using Autonomous Driving Scenarios Derived from
a Formal Model’. In: Journal of Intelligent and Robotic Systems 107.4 (Apr. 2023), pp. 1–45. DOI:
10.1007/s10846-023-01808-3. URL: https://inria.hal.science/hal-04138579.

[17] M. Kabtoul, M. Prédhumeau, A. Spalanzani, J. Dugdale and P. Martinet. ‘How To Evaluate the
Navigation of Autonomous Vehicles Around Pedestrians?’ In: IEEE Transactions on Intelligent
Transportation Systems (23rd Oct. 2023), pp. 1–11. DOI: 10.1109/TITS.2023.3323662. URL:
https://inria.hal.science/hal-04255479.

[18] X. Peng, O. Simonin and C. Solnon. ‘Non-Crossing Anonymous MAPF for Tethered Robots’. In:
Journal of Artificial Intelligence Research 78 (Oct. 2023), pp. 357–384. DOI: 10.1613/jair.1.143
51. URL: https://hal.science/hal-04216937.

[19] A. Savina, T. Jaffredo, F. Saldmann, C. Faulkes, P. Moguelet, C. Leroy, D. D. Marmol, P. Codogno,
L. Foucher, A. Zalc, M. Viltard, G. Friedlander, S. Aractingi and R. Fontaine. ‘Single-cell tran-
scriptomics reveals age-resistant maintenance of cell identities, stem cell compartments and
differentiation trajectories in long-lived naked mole-rats skin’. In: Aging (Albany N.Y.) 14.9 (7th Feb.
2023), pp. 3728–3756. DOI: 10.18632/aging.204054. URL: https://cnrs.hal.science/hal-
03977899.

[20] P. T. Singamaneni, P. Bachiller-Burgos, L. J. Manso, A. Garrell, A. Sanfeliu, A. Spalanzani and R.
Alami. ‘A Survey on Socially Aware Robot Navigation: Taxonomy and Future Challenges’. In: The
International Journal of Robotics Research (2024), pp. 1–33. URL: https://inria.hal.science
/hal-04421740.

International peer-reviewed conferences

[21] N. Ahmad, M. Egan, J.-M. Gorce, J. Dibangoye and F. Le Mouël. ‘Optimization of Sensor Config-
urations for Fault Identification in Smart Buildings’. In: ICASSP 2023 - 2023 IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP). Rhodes Island, United States:
IEEE, 2023, pp. 1–5. DOI: 10.1109/ICASSP49357.2023.10097223. URL: https://hal.scienc
e/hal-04361063.

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[22] S. Ahmed, F. Le Mouël, N. Stouls and J. Dibangoye. ‘R-MDP: A Game Theory Approach for Fault-
Tolerant Data and Service Management in Crude Oil Pipelines Monitoring Systems’. In: MobiQuit-
ous 2022 - 19th EAI International Conference on Mobile and Ubiquitous Systems: Computing,
Networking and Services. Pittsburgh, United States, 12th Jan. 2023. URL: https://inria.hal.s
cience/hal-03912306.

[23] T. Arrabal, M. Stojanova, I. Guérin-Lassous and J. Picot. ‘Experiment-driven platform for link
quality estimation in IEEE 802.11 WLANs’. In: HPSR 2023 - IEEE 24th International Conference on
High-Performance Switching and Routing. Albuquerque, NM, United States, 2023, pp. 1–6. URL:
https://hal.science/hal-04089954.

[24] R. Asghar, M. A. Diaz-Zapata, L. Rummelhard, A. Spalanzani and C. Laugier. ‘Vehicle Motion
Forecasting using Prior Information and Semantic-assisted Occupancy Grid Maps’. In: awaiting
publication on IEEE Xplore. IROS 2023 - IEEE/RSJ International Conference on Intelligent Robots
and Systems. Detroit, Michigan, United States: IEEE, 5th Oct. 2023, pp. 1–6. URL: https://inria
.hal.science/hal-04331648.

[25] S. d’Alu, H. Rivano and O. Simonin. ‘TDoA for in-flight relative localization in UAV swarm using
Ultra-Wide Band’. In: CEUR Workshop Proceedings. IPIN 2023 - 13th International Conference
on Indoor Positioning and Indoor Navigation. IPIN-WiP 2023 Indoor Positioning and Indoor
Navigation - Work-in-Progress Papers 2023. Nuremberg, Germany, Sept. 2023. URL: https://hal
.science/hal-04240297.

[26] A. Delage, O. Buffet and J. Dibangoye. ‘Global min-max Computation for α-Hölder Games’. In:
Proceedings of the 35th IEEE International Conference on Tools with Artificial Intelligence (ICTAI).
ICTAI 2023 - 35th IEEE International Conference on Tools with Artificial Intelligence. Atlanta
(Georgia), United States: IEEE Computer Society, 8th Nov. 2023, pp. 518–525. URL: https://inri
a.hal.science/hal-04382880.

[27] F. Delobel, P. Derbez, A. Gontier, L. Rouquette and C. Solnon. ‘A CP-based Automatic Tool for
Instantiating Truncated Differential Characteristics ⋆’. In: LNCS. INDOCRYPT 2023 - 24th In-
ternational Conference on Cryptology in India. Goa, India: Springer, Dec. 2023, pp. 1–23. URL:
https://hal.science/hal-04343593.

[28] M. A. Diaz-Zapata, D. Sierra González, Ö. Erkent, J. Dibangoye and C. Laugier. ‘LAPTNet-FPN:
Multi-scale LiDAR-aided Projective Transform Network for Real Time Semantic Grid Prediction’.
In: Proceedings of the 2023 IEEE International Conference on Robotics and Automation (ICRA
2023). ICRA 2023 - IEEE International Conference on Robotics and Automation. Londres, United
Kingdom: IEEE, 29th May 2023, pp. 1–6. URL: https://hal.science/hal-03980399.

[29] E. Escudie, L. Matignon and J. Saraydaryan. ‘Attention Graph for Multi-Robot Social Naviga-
tion with Deep Reinforcement Learning’. In: Proceedings of the 23rd International Conference on
Autonomous Agents and Multiagent Systems. AAMAS 2024 - International Conference on Autonom-
ous Agents and Multi-Agent Systems. Auckland, New Zealand, 2024. URL: https://hal.scienc
e/hal-04427749.

[30] T. Genevois, L. Rummelhard, A. Spalanzani and C. Laugier. ‘From Probabilistic Occupancy Grids
to versatile Collision Avoidance using Predictive Collision Detection’. In: 2023 IEEE International
Conference on Intelligent Transportation Systems (ITSC). ITSC 2023 - IEEE International Confer-
ence on Intelligent Transportation Systems. Bilbao, Spain: IEEE, 27th Sept. 2023, pp. 1–7. URL:
https://inria.hal.science/hal-04225028.

[31] T. Genevois, A. Spalanzani and C. Laugier. ‘Interaction-aware Predictive Collision Detector for
Human-aware Collision Avoidance’. In: 2023 IEEE Intelligent Vehicles Symposium (IV). IV 2023 -
IEEE Intelligent Vehicles Symposium. Anchorage, United States: IEEE, 4th June 2023, pp. 1–7. DOI:
10.1109/IV55152.2023.10186778. URL: https://inria.hal.science/hal-04173625.

[32]

J.-B. Horel, R. Baruffa, L. Rummelhard, A. Renzaglia and C. Laugier. ‘A Navigation-Based Evalu-
ation Metric for Probabilistic Occupancy Grids: Pathfinding Cost Mean Squared Error’. In: IEEE
International Conference on Intelligent Transportation Systems. ITCS 2023 - 26th IEEE Interna-
tional Conference on Intelligent Transportation Systems. Bilbao, Spain, Spain: IEEE, 2023, pp. 1–6.
URL: https://hal.science/hal-04211125.

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[33] P. Marza, L. Matignon, O. Simonin and C. Wolf. ‘Multi-Object Navigation with dynamically learned
neural implicit representations’. In: ICCV 2023 - International Conference on Computer Vision.
Paris, France, 2023, pp. 1–21. URL: https://hal.science/hal-04390240.

[34] X. Peng and C. Solnon. ‘Using Canonical Codes to Efficiently Solve the Benzenoid Generation
Problem with Constraint Programming’. In: International Conference on Principles and Practice of
Constraint Programming (CP). CP 2023 - 29th International Conference on Principles and Practice
of Constraint Programming. Toronto (CA), Canada: LIPIcs, 2023. DOI: 10.4230/LIPIcs.CP.202
3.17. URL: https://hal.science/hal-04156847.

[35]

J. Saraydaryan, F. Jumel and O. Simonin. ‘Human Presence Probability Map (HPP): a Probability
propagation based on Human Flow Grid’. In: RoboCup 2023 - 26e symposium international
RoboCup. Vol. RoboCup 2023: Robot World Cup XXV. Bordeaux, France, 2023. URL: https://hal
.science/hal-04408763.

National peer-reviewed Conferences

[36] T. Balaguer, O. Simonin, I. G. Lassous and I. Fantoni. ‘Etat de l’art sur la co-simulation robotique
et réseau des systèmes multi-robots’. In: Journées Francophones des Systèmes Multi-Agents. JFSM
2023 - 31èmes Journées Francophones des Systèmes Multi-Agents. Strasbourg, France, 2023,
pp. 1–10. URL: https://hal.science/hal-04105508.

Conferences without proceedings

[37] A. Delage, O. Buffet and J. Dibangoye. ‘Global Min-Max Computation for α-Hölder Zero-Sum
Games’. In: GAIW 2023 - 5th Games, Agents, and Incentives Workshop. Londres (London), United
Kingdom, 2023, pp. 1–9. URL: https://inria.hal.science/hal-04382817.

[38] A. Delage, O. Buffet, J. Dibangoye and A. Saffidine. ‘Heuristic Search Value Iteration can solve
zero-sum Partially Observable Stochastic Games’. In: MSDM 202311th Multiagent Sequential
Decision Making under Uncertainty Workshop ; Held as part of the Workshops at the IFAAMAS
2023 - 21st International Conference on Autonomous Agents and Multiagent Systems. Londres,
United Kingdom, May 2023. URL: https://inria.hal.science/hal-04382922.

Doctoral dissertations and habilitation theses

[39] A. Bonnefond. ‘Flocking models based on local communications : From theory to simulations’.

INSA de Lyon, 14th June 2023. URL: https://theses.hal.science/tel-04286379.

[40] B. Renault. ‘Navigation Among Movable Obstacles (NAMO) Extended to Social and Multi-Robot

Constraints’. Insa Lyon, 19th Dec. 2023. URL: https://hal.science/tel-04418723.

Reports & preprints

[41] A. Martinelli. Detection of a serious error in the paper: "On identifiability of nonlinear ODE models
and applications in viral dynamics". 26th Jan. 2024. URL: https://inria.hal.science/hal-0
4419008.

[42] A. Martinelli. General analytical condition to nonlinear identifiability and its application in viral

dynamics. 23rd Jan. 2024. URL: https://inria.hal.science/hal-04413371.

[43] A. Martinelli. Identifiability of nonlinear ODE Models with Time-Varying Parameters: the General
Analytical Solution and Applications in Viral Dynamics. 4th Dec. 2023. URL: https://inria.hal
.science/hal-04420973.

Other scientific publications

[44] R. Asghar, M. A. Diaz-Zapata, L. Rummelhard, A. Spalanzani, A. Spalanzani and C. Laugier. ‘Vehicle
Motion Forecasting using Prior Information and Semantic-assisted Occupancy Grid Maps’. In:
NAVER LABS 2023 - Europe International Workshop on AI for Robotics. Grenoble, France, 15th Nov.
2023. URL: https://hal.science/hal-04416326.

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[45] T. Genevois, A. Spalanzani and C. Laugier. Video supplement for : "Interaction-aware Predictive
Collision Detector for Human-aware Collision Avoidance", T. Genevois, A. Spalanzani and C.
Laugier, in IEEE Intelligent Vehicles Symposium (IV), 2023. 18th Apr. 2023. URL: https://inria
.hal.science/hal-04073269.

12.3 Other

Scientific popularization

[46] C. Solnon. ‘Déconstruire le mythe de l’immatérialité du numérique’. In: La Recherche (July 2023).

URL: https://hal.science/hal-04194044.

[47] C. Solnon. ‘Des algorithmes pour expliquer les algorithmes’. In: La Recherche (Apr. 2023). URL:

https://hal.science/hal-04194042.

[48] C. Solnon. ‘L’art d’écrire des programmes’. In: La Recherche (Jan. 2023). URL: https://hal.scie

nce/hal-03941278.

[49] C. Solnon. ‘Le meilleur des deux mondes’. In: La Recherche (2023). URL: https://hal.science

/hal-04209801.

[50] C. Solnon. ‘Optimiser, mais pour qui ?’ In: La Recherche (Jan. 2024). URL: https://hal.science

/hal-04343599.

12.4 Cited publications

[51] R. Asghar, M. Garzón, J. Lussereau and C. Laugier. ‘Vehicle Localization Based on Visual Lane
Marking and Topological Map Matching’. In: ICRA 2020 - IEEE International Conference on Robot-
ics and Automation. Paris, France: IEEE, May 2020, pp. 258–264. DOI: 10.1109/ICRA40945.2020
.9197543. URL: https://hal.inria.fr/hal-03043594.

[52] R. Asghar, L. Rummelhard, A. Spalanzani and C. Laugier. ‘Allo-centric Occupancy Grid Prediction
for Urban Traffic Scene Using Video Prediction Networks’. In: ICARCV 2022 - 17th International
Conference on Control, Automation, Robotics and Vision. Singapore, Singapore, 2022. URL: https:
//inria.hal.science/hal-03862095.

[53] M. Barbier. ‘Crossing of Road Intersections: Decision-Making Under Uncertainty for Autonomous
Vehicles’. Theses. Comue Université Grenoble Alpes, Dec. 2019. URL: https://hal.inria.fr/t
el-02424655.

[54] M. Barbier, J. Ibañez-Guzmán, C. Laugier and O. Simonin. ‘Validation Framework Applied to the
Decision-Making Process for an Autonomous Vehicle Crossing Road Intersections’. In: Validation
and Verification of Automated Systems. Springer International Publishing, Nov. 2020, pp. 179–205.
DOI: 10.1007/978-3-030-14628-3\_15. URL: https://hal.inria.fr/hal-02424651.

[55] M. Barbier, A. Renzaglia, J. Quilbeuf, L. Rummelhard, A. Paigwar, C. Laugier, A. Legay, J. Ibañez-
Guzmán and O. Simonin. ‘Validation of Perception and Decision-Making Systems for Autonomous
Driving via Statistical Model Checking’. In: IV 2019 - 30th IEEE Intelligent Vehicles Symposium.
Paris, France: IEEE, June 2019, pp. 252–259. DOI: 10.1109/IVS.2019.8813793. URL: https://h
al.inria.fr/hal-02127889.

[56] E. Beeching, J. Dibangoye, O. Simonin and C. Wolf. ‘EgoMap: Projective mapping and structured
egocentric memory for Deep RL’. In: ECML-PKDD 2020 - European Conference on Machine
Learning and Principles and Practice of Knowledge Discovery in Databases. https://arxiv.org
/abs/2002.02286. ghent, Belgium, Sept. 2020, pp. 1–12. URL: https://hal.archives-ouvert
es.fr/hal-02864146.

[57] E. Beeching, J. Dibangoye, O. Simonin and C. Wolf. ‘Learning to plan with uncertain topological
maps’. In: ECCV 2020 - 16th European Conference on Computer Vision. Glasgow, United Kingdom,
Aug. 2020, pp. 1–24. URL: https://hal.inria.fr/hal-02933641.

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[58] E. Beeching, C. Wolf, J. Dibangoye and O. Simonin. ‘Deep Reinforcement Learning on a Budget: 3D
Control and Reasoning Without a Supercomputer’. In: ICPR 2020 - 25th International Conference
on Pattern Recognition. Milan, Italy, Dec. 2020, pp. 1–16. URL: https://hal.inria.fr/hal-02
934130.

[59] A. Bonnefond, O. Simonin and I. Guérin-Lassous. ‘Extension of Flocking Models to Environments
with Obstacles and Degraded Communications’. In: IROS 2021 - IEEE/RSJ International Conference
on Intelligent Robots and Systems. Proceedings IROS 2021. Prague / Virtual, Czech Republic: IEEE,
Sept. 2021, pp. 9139–9145. DOI: 10.1109/IROS51168.2021.9635944. URL: https://hal.scie
nce/hal-03365129.

[60] G. Bono. ‘Deep multi-agent reinforcement learning for dynamic and stochastic vehicle routing
problems’. Theses. Université de Lyon, Oct. 2020. URL: https://tel.archives-ouvertes.fr
/tel-03098433.

[61] L. Briñon Arranz, A. Renzaglia and L. Schenato. ‘Multi-Robot Symmetric Formations for Gradient
and Hessian Estimation with Application to Source Seeking’. In: IEEE Transactions on Robotics
35.3 (June 2019), pp. 782–789. DOI: 10.1109/TRO.2019.2895509. URL: https://hal.inria.f
r/hal-01991153.

[62] O. Buffet, J. Dibangoye, A. Delage, A. Saffidine and V. Thomas. ‘On Bellman’s Optimality Principle
for zs-POSGs’. https://arxiv.org/abs/2006.16395v1 - working paper or preprint. Dec. 2020.
URL: https://hal.inria.fr/hal-03080287.

[63] O. Buffet, J. Dibangoye, A. Delage, A. Saffidine and V. Thomas. ‘Sur le principe d’optimalité de
Bellman pour les zs-POSG’. In: JFPDA 2020 - Journées Francophones surla Planification, la Décision
et l’Apprentissagepour la conduite de systèmes. Angers (virtuel), France, June 2020, pp. 1–3. URL:
https://hal.inria.fr/hal-03081320.

[64] O. Buffet, J. Dibangoye, A. Saffidine and V. Thomas. ‘Heuristic Search Value Iteration for zero-sum
Stochastic Games’. In: IEEE Transactions on Games 13.3 (2021), pp. 1–10. DOI: 10.1109/TG.2020
.3005214. URL: https://hal.inria.fr/hal-03080314.

[65]

[66]

J. Civera, D. Galvez-Lopez, L. Riazuelo, J. D. Tardós and J. M. M. Montiel. ‘Towards semantic SLAM
using a monocular camera’. In: IROS. 2011.

J.-Y. Courtonne, M. Boissier, A. Borthomieu, L. Fauste, V. Jost, E. Krieger, G. Mandil, M. Mangeot,
O. Mauviel, C. Solnon and P. Sturm. ‘Des outils numériques d’aide à la décision pour la conception
et l’évaluation participative d’organisations alternatives de l’économie’. In: ROADEF 2022 - 23ème
congrès annuel de la Société Française de Recherche Opérationnelle et d’Aide à la Décision. INSA
Lyon. Villeurbanne - Lyon, France, Feb. 2022, pp. 1–2. URL: https://hal.science/hal-035952
73.

[67] S. d’Alu, O. Iova, O. Simonin and H. Rivano. ‘Demo: In-flight Localisation of Micro-UAVs using
Ultra-Wide Band’. In: EWSN 2020 - International Conference on Embedded Wireless Systems and
Networks. Lyon, France: ACM, Feb. 2020.

[68] M. A. Diaz-Zapata, Ö. Erkent and C. Laugier. ‘Instance Segmentation with Unsupervised Ad-
aptation to Different Domains for Autonomous Vehicles’. In: ICARCV 2020 - 16th International
Conference on Control, Automation, Robotics and Vision. Shenzen, China, Dec. 2020, pp. 1–7. URL:
https://hal.inria.fr/hal-03041432.

[69] M. A. Diaz-Zapata, Ö. Erkent, C. Laugier, J. Dibangoye and D. Sierra González. ‘LAPTNet: LiDAR-
Aided Perspective Transform Network’. In: ICARCV 2022 - 17th International Conference on Control,
Automation, Robotics and Vision. Singapore, Singapore, 2022. URL: https://inria.hal.scien
ce/hal-03851513.

[70]

J. S. Dibangoye and O. Buffet. ‘Learning to Act in Decentralized Partially Observable MDPs’. In:
ICML 2018 - 35th International Conference on Machine Learning. Vol. 80. Proceedings of Machine
Learning Research. Stockholm, Sweden: PMLR, 2018, pp. 1233–1242.

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[71] Ö. Erkent and C. Laugier. ‘Semantic Segmentation with Unsupervised Domain Adaptation Under
Varying Weather Conditions for Autonomous Vehicles’. In: IEEE Robotics and Automation Letters
(Mar. 2020), pp. 1–8. DOI: 10.1109/LRA.2020.2978666. URL: https://hal.inria.fr/hal-02
502457.

[72] Ö. Erkent, D. Sierra Gonzalez, A. Paigwar and C. Laugier. ‘GridTrack: Detection and Tracking of
Multiple Objects in Dynamic Occupancy Grids’. In: ICVS 2021 - International Conference on Vision
Systems. Virtual Conference, Austria, Oct. 2021, pp. 1–14. DOI: 10.1007/978-3-030-87156-7
\_15. URL: https://hal.inria.fr/hal-03335282.

[73] Ö. Erkent, C. Wolf and C. Laugier. ‘End-to-End Learning of Semantic Grid Estimation Deep
Neural Network with Occupancy Grids’. In: Unmanned systems 7.3 (July 2019), pp. 171–181. DOI:
10.1142/S2301385019410036. URL: https://hal.archives-ouvertes.fr/hal-02302533.

[74] Ö. Erkent, C. Wolf and C. Laugier. ‘Semantic Grid Estimation with Occupancy Grids and Semantic
Segmentation Networks’. In: ICARCV 2018 - 15th International Conference on Control, Automation,
Robotics and Vision. Singapore, Singapore, Nov. 2018, pp. 1–6. URL: https://hal.inria.fr/ha
l-01933939.

[75] Ö. Erkent, C. Wolf, C. Laugier, D. Sierra González and V. R. Cano. ‘Semantic Grid Estimation with
a Hybrid Bayesian and Deep Neural Network Approach’. In: IROS 2018 - IEEE/RSJ International
Conference on Intelligent Robots and Systems. Madrid, Spain: IEEE, Oct. 2018, pp. 1–8. URL: https:
//hal.inria.fr/hal-01881377.

[76] L. Fauste. ‘Aide au choix d’échelle géographique dans l’industrie productive par l’optimisation
sous contraintes’. In: ROADEF 2022 - 23ème congrès annuel de la Société Française de Recherche
Opérationnelle et d’Aide à la Décision. INSA Lyon. Villeurbanne - Lyon, France, 2022, pp. 1–2. URL:
https://hal.science/hal-03560468.

[77] M. Fehr, O. Buffet, V. Thomas and J. Dibangoye. ‘ρ-POMDPs have Lipschitz-Continuous ϵ-Optimal
Value Functions’. In: NIPS 2018 - Thirty-second Conference on Neural Information Processing
Systems. Montréal, Canada, Dec. 2018. URL: https://hal.inria.fr/hal-01903685.

[78] R. Fontaine, C. Solnon and J. Dibangoye. ‘Approche hybride de résolution pour le Time-Dependent
Traveling Salesman Problem with Time Windows’. In: 23ème congrès annuel de la Société Française
de Recherche Opérationnelle et d’Aide à la Décision. INSA Lyon. Villeurbanne - Lyon, France, Feb.
2022. URL: https://hal.archives-ouvertes.fr/hal-03595249.

[79] P. Grippa, A. Renzaglia, A. Rochebois, M. Schranz and O. Simonin. ‘Inspection of Ship Hulls with
Multiple UAVs: Exploiting Prior Information for Online Path Planning’. In: IROS 2022 - IEEE/RSJ
International Conference on Intelligent Robots and Systems. Kyoto, Japan: IEEE, 2022. URL: https:
//inria.hal.science/hal-03776012.

[80] R. Grünblatt, I. Guérin-Lassous and O. Simonin. ‘A distributed antenna orientation solution for
optimizing communications in a fleet of UAVs’. In: Computer Communications 181 (Jan. 2022),
pp. 102–115. DOI: 10.1016/j.comcom.2021.09.020. URL: https://hal.science/hal-0340
0498.

[81]

J.-B. Horel, C. Laugier, L. Marsso, R. Mateescu, L. Muller, A. Paigwar, A. Renzaglia and W. Serwe.
‘Using formal conformance testing to generate scenarios for autonomous vehicles’. In: 2022
Design, Automation & Test in Europe Conference & Exhibition (DATE). 2022, pp. 532–537.

[82] F. Jumel, J. Saraydaryan, R. Leber, L. Matignon, E. Lombardi, C. Wolf and O. Simonin. ‘Context
Aware Robot Architecture, Application to the RoboCup@Home Challenge’. In: RoboCup sym-
posium. Montreal, Canada, June 2018, pp. 1–12. URL: https://hal.archives-ouvertes.fr/h
al-01832613.

[83] M. Kabtoul, P. Martinet and A. Spalanzani. ‘Proactive Longitudinal Velocity Control In Pedestrians-
Vehicle Interaction Scenarios’. In: ITSC 2020 - 23rd IEEE International Conference on Intelligent
Transportation Systems. Rhodes, Greece: IEEE, Sept. 2020, pp. 1–6. URL: https://hal.inria.fr
/hal-02944369.

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[84] M. Kabtoul, A. Spalanzani and P. Martinet. ‘Proactive And Smooth Maneuvering For Navigation
Around Pedestrians’. In: ICRA 2022 - IEEE International Conference on Robotics and Automation.
Philadelphia, United States, 2022, pp. 1–7. URL: https://inria.hal.science/hal-03596570.

[85] M. Kabtoul, A. Spalanzani and P. Martinet. ‘Towards Proactive Navigation: A Pedestrian-Vehicle
Cooperation Based Behavioral Model’. In: ICRA 2020 - IEEE International Conference on Robotics
and Automation. IEEE International Conference on Robotics and Automation Proceedings. Paris,
France, May 2020, pp. 6958–6964. URL: https://hal.archives-ouvertes.fr/hal-02509637.

[86] V. Lallemand, M. Minier and L. Rouquette. ‘Automatic Search of Rectangle Attacks on Feistel
Ciphers: Application to WARP’. In: IACR Transactions on Symmetric Cryptology 2022.2 (June 2022),
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<scratchpad>
- Main topic: Research Centre CHROMA, focusing on perception, decision-making, and cooperation for autonomous navigation and intelligent transport.
- Key points:
  - Project-team CHROMA was launched in 2015 and became an Inria project-team in 2017.
  - The team aims to address fundamental and open issues in human-centered robotics, multi-robot systems, and AI for humanity.
  - Research themes include perception, cognition, and interaction; decision-making; and robotics and smart environments.
  - The team collaborates with various laboratories and companies, including Toyota, Renault, and the European Union.
- Structure: The document is divided into sections, including Research Centres, Activity Report, Project-Team CHROMA, and more.
- Potential artifacts to ignore: Web-related elements such as links, HTML tags, and unnecessary text.
</scratchpad>

<final_summary>
The CHROMA project-team at Inria Lyon focuses on research in perception, decision-making, and cooperation for autonomous navigation and intelligent transport. The team aims to address fundamental and open issues in human-centered robotics, multi-robot systems, and AI for humanity. With collaborations with various laboratories and companies, CHROMA seeks to develop algorithms and models that enable autonomous agents to perceive, decide, learn, and adapt to their environment. The team's research is organized into two main axes: perception and situation awareness, and decision-making. Key achievements include the development of Bayesian perception methods, decision-making algorithms, and swarm robotics approaches. CHROMA's research has significant implications for the development of autonomous vehicles, service robotics, and other applications in intelligent transport and robotics.
The CHROMA project-team at Inria Lyon focuses on research in perception, decision-making, and cooperation for autonomous navigation and intelligent transport. The team aims to address fundamental and open issues in human-centered robotics, multi-robot systems, and AI for humanity. With collaborations with various laboratories and companies, CHROMA seeks to develop algorithms and models that enable autonomous agents to perceive, decide, learn, and adapt to their environment. The team's research is organized into two main axes: perception and situation awareness, and decision-making. Key achievements include the development of Bayesian perception methods, decision-making algorithms, and swarm robotics approaches. CHROMA's research has significant implications for the development of autonomous vehicles, service robotics, and other applications in intelligent transport and robotics.