README.md

metadata

license: apache-2.0
task_categories:
  - robotics
  - keypoint-detection
tags:
  - robot-manipulation
  - vision-language-models
  - zero-shot-generalization
  - bridge-v2

PEEK VLM-Labeled BRIDGE_v2 dataset

This dataset contains the LeRobot-format BRIDGE-v2 dataset with paths and masks from the PEEK VLM drawn onto the image: PEEK: Guiding and Minimal Image Representations for Zero-Shot Generalization of Robot Manipulation Policies.

PEEK fine-tunes Vision-Language Models (VLMs) to predict a unified point-based intermediate representation for robot manipulation. This representation consists of:

1. End-effector paths: specifying what actions to take.
2. Task-relevant masks: indicating where to focus.

These annotations are directly overlaid onto robot observations, making the representation policy-agnostic and transferable across architectures. This dataset provides these automatically generated labels for the BRIDGE_v2 dataset, enabling researchers to readily use them for policy training and enhancement to boost zero-shot generalization.

Paper

PEEK: Guiding and Minimal Image Representations for Zero-Shot Generalization of Robot Manipulation Policies

Project Page

https://peek-robot.github.io

Code/Github Repository

The main PEEK framework and associated code can be found on the Github repository: https://github.com/peek-robot/peek

Sample Usage

This dataset provides the BRIDGE_v2 dataset labeled with PEEK VLM path and mask labels.

Citation

If you find this dataset useful for your research, please cite the original paper:

@inproceedings{zhang2025peek,
    title={PEEK: Guiding and Minimal Image Representations for Zero-Shot Generalization of Robot Manipulation Policies}, 
    author={Jesse Zhang and Marius Memmel and Kevin Kim and Dieter Fox and Jesse Thomason and Fabio Ramos and Erdem Bıyık and Abhishek Gupta and Anqi Li},
    booktitle={arXiv:2509.18282},
    year={2025},
}