task_categories:
- robotics
tags:
- humanoid
- world-model
- contact-planning
- ego-vision
EgoVCP Dataset
This repository contains the dataset for the paper Ego-Vision World Model for Humanoid Contact Planning.
Project Page | GitHub | arXiv
The EgoVCP dataset is a demonstration-free offline dataset used to train a learned world model for humanoid contact planning. It features data collected from a G1 humanoid robot in the Isaac Lab simulation environment, enabling the robot to predict outcomes in a compressed latent space for contact-aware tasks.
Dataset Tasks
The dataset includes rollouts for three primary manipulation and navigation scenarios:
- Wall Support (
wall): Supporting the robot against a wall after a perturbation. - Ball Blocking (
ball): Interacting with and blocking incoming objects (e.g., a yoga ball). - Tunnel Traversal (
tunnel): Traversing height-limited arches or tunnels.
Dataset Structure
The dataset contains:
- Ego-centric Depth Images: Captured from the robot's perspective.
- Proprioception Data: Internal robot state information.
- Actions and Rewards: For offline world model training.
The data is organized by task into subdirectories: wall, ball, and tunnel.
Usage
To use this dataset with the official implementation, you can clone it directly into your project directory:
cd Ego-VCP
mkdir dataset
git clone https://huggingface.co/datasets/Hang917/EgoVCP_Dataset.git dataset
Citation
If you use this dataset in your research, please cite the following paper:
@article{liu2025egovcp,
title={Ego-Vision World Model for Humanoid Contact Planning},
author={Liu, Hang and others},
journal={arXiv preprint arXiv:2510.11682},
year={2025}
}