Datasets:

EXOKERN
/

contactbench-forge-peginsert-v0

pretty_name: EXOKERN ContactBench v0 - Peg Insertion with Force/Torque
license: cc-by-nc-4.0
language:
  - en
task_categories:
  - robotics
size_categories:
  - 1K<n<10K
tags:
  - robotics
  - force-torque
  - contact-rich
  - manipulation
  - insertion
  - lerobot
  - isaac-lab
  - forge
  - franka
  - panda
  - simulation
  - benchmark
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/**/*.parquet
dataset_info:
  features:
    - name: observation.state
      dtype: float32
      shape:
        - 24
    - name: observation.wrench
      dtype: float32
      shape:
        - 6
    - name: action
      dtype: float32
      shape:
        - 7
    - name: timestamp
      dtype: float32
    - name: frame_index
      dtype: int64
    - name: episode_index
      dtype: int64
    - name: index
      dtype: int64
    - name: task_index
      dtype: int64
  splits:
    - name: train
      num_examples: 330929

EXOKERN ContactBench v0 - Peg Insertion with Force/Torque

contactbench-forge-peginsert-v0 is the fixed-condition baseline dataset in the EXOKERN ContactBench series. It is designed for clean ablations of force/torque sensing before adding domain randomization.

Quick Facts

Item	Value
Episodes	2,221
Frames	330,929
Control frequency	20 Hz
Format	LeRobot v3.0-compatible parquet
Robot	Franka Emika Panda
Simulator	NVIDIA Isaac Lab
Task	`Isaac-Forge-PegInsert-Direct-v0`
Access	Gated download on the Hugging Face Hub

Why This Release Exists

Most open manipulation datasets focus on geometry and vision while leaving contact dynamics implicit. v0 exists as a controlled reference dataset where the wrench signal is explicit and the environment is mostly fixed, making it easier to isolate the effect of force/torque input during policy training.

This dataset is the paired data source for EXOKERN/skill-forge-peginsert-v0.

Data Schema

Field	Shape	Description
`observation.state`	`(24,)`	Collapsed policy state vector exported by the collection task
`observation.wrench`	`(6,)`	End-effector wrench `[Fx, Fy, Fz, Mx, My, Mz]` in N / N*m
`action`	`(7,)`	Normalized controller action recorded during data collection
`timestamp`	scalar	Per-frame timestamp in seconds
`frame_index`	scalar	Frame index within an episode
`episode_index`	scalar	Episode identifier
`index`	scalar	Global frame index
`task_index`	scalar	LeRobot task mapping index

State Tensor Semantics

The observation.state vector is a 24D flat tensor exported by the FORGE collection pipeline. The current mapping is:

Index Range	Field	Dim	Unit
`0:3`	`fingertip_pos`	3	m
`3:6`	`fingertip_pos_rel_fixed`	3	m
`6:10`	`fingertip_quat`	4	unitless
`10:13`	`ee_linvel`	3	m/s
`13:16`	`ee_angvel`	3	rad/s
`16:22`	`force_sensor_smooth`	6	N / N*m
`22`	`force_threshold`	1	N
`23`	`ema_factor`	1	unitless

observation.wrench duplicates observation.state[16:22] as a dedicated column for direct access.

Wrench Convention

Property	Value
Frame	End-effector body frame
Order	`[Fx, Fy, Fz, Mx, My, Mz]`
Force unit	Newtons
Torque unit	Newton-meters

Why v0 Is Useful

It is the cleanest EXOKERN release for force/torque ablations.
The collection regime is simple enough that contact-quality differences are easy to interpret.
It pairs directly with a public baseline model repo.

Reference benchmark from EXOKERN/skill-forge-peginsert-v0:

Condition	Success Rate	Avg Contact Force (N)
`no_ft`	100.0	5.2
`full_ft`	100.0	3.2

Figure: representative wrench trace plus episode-wise mean and peak force coverage computed from the published parquet rollout data.

Loading

from lerobot.datasets.lerobot_dataset import LeRobotDataset

ds = LeRobotDataset("EXOKERN/contactbench-forge-peginsert-v0")
print(ds.num_episodes, len(ds))

frame = ds[0]
state = frame["observation.state"]
wrench = frame["observation.wrench"]
action = frame["action"]

print(state.shape, wrench.shape, action.shape)

from datasets import load_dataset

ds = load_dataset("EXOKERN/contactbench-forge-peginsert-v0", split="train")
print(ds[0].keys())

Related Work

FORGE: Force-Guided Exploration for Robust Contact-Rich Manipulation under Uncertainty
Factory: Fast Contact for Robotic Assembly
Diffusion Policy: Visuomotor Policy Learning via Action Diffusion

Citation

@dataset{exokern_contactbench_peginsert_v0_2026,
  title   = {EXOKERN ContactBench v0: Peg Insertion with Force/Torque},
  author  = {{EXOKERN}},
  year    = {2026},
  url     = {https://huggingface.co/datasets/EXOKERN/contactbench-forge-peginsert-v0},
  license = {CC-BY-NC-4.0}
}

Limitations

Simulation only. The dataset is not a real-robot capture.
This is a mostly fixed-condition release, so it is not the right dataset for robustness claims under large physical variation.
The task scope is a single peg-insertion setup.
Access is gated on the Hub.

Related Resources

Baseline model: EXOKERN/skill-forge-peginsert-v0
Domain-randomized successor: EXOKERN/contactbench-forge-peginsert-v0.1.1
Organization page: huggingface.co/EXOKERN