metadata
license: mit
task_categories:
- robotics
language:
- en
tags:
- code
pretty_name: GEMBench dataset
size_categories:
- 10K<n<100K
Dataset Card for GEMBench dataset
💎 GEneralizable vision-language robotic Manipulation Benchmark Dataset
A benchmark to systematically evaluate generalization capabilities of vision-and-language robotic manipulation policies. Built upon the RLBench simulator.
💻 GEMBench Project Webpage: https://www.di.ens.fr/willow/research/gembench/
📈 Leaderboard: https://paperswithcode.com/sota/robot-manipulation-generalization-on-gembench
Dataset Structure
Dataset structure is as follows:
- gembench
- train_dataset
- microsteps: 567M, initial configurations for each episode
- keysteps_bbox: 160G, extracted keysteps data
- keysteps_bbox_pcd: (used to train 3D-LOTUS)
- voxel1cm: 10G, processed point clouds
- instr_embeds_clip.npy: instructions encoded by CLIP text encoder
- motion_keysteps_bbox_pcd: (used to train 3D-LOTUS++ motion planner)
- voxel1cm: 2.8G, processed point clouds
- action_embeds_clip.npy: action names encoded by CLIP text encoder
- val_dataset
- microsteps: 110M, initial configurations for each episode
- keysteps_bbox_pcd:
- voxel1cm: 941M, processed point clouds
- test_dataset
- microsteps: 2.2G, initial configurations for each episode
🛠️ Benchmark Installation
- Create and activate your conda environment:
conda create -n gembench python==3.10
conda activate gembench
- Install RLBench Download CoppeliaSim (see instructions here)
# change the version if necessary
wget https://www.coppeliarobotics.com/files/V4_1_0/CoppeliaSim_Edu_V4_1_0_Ubuntu20_04.tar.xz
tar -xvf CoppeliaSim_Edu_V4_1_0_Ubuntu20_04.tar.xz
Add the following to your ~/.bashrc file:
export COPPELIASIM_ROOT=$(pwd)/CoppeliaSim_Edu_V4_1_0_Ubuntu20_04
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$COPPELIASIM_ROOT
export QT_QPA_PLATFORM_PLUGIN_PATH=$COPPELIASIM_ROOT
Install Pyrep and RLBench
git clone https://github.com/cshizhe/PyRep.git
cd PyRep
pip install -r requirements.txt
pip install .
cd ..
# Our modified version of RLBench to support new tasks in GemBench
git clone https://github.com/rjgpinel/RLBench
cd RLBench
pip install -r requirements.txt
pip install .
cd ..
Evaluation
Please, check 3D-LOTUS++ official code repository for evaluation:
https://github.com/vlc-robot/robot-3dlotus?tab=readme-ov-file#evaluation
Citation
If you use our GemBench benchmark or find our code helpful, please kindly cite our work:
BibTeX:
@inproceedings{garcia25gembench,
author = {Ricardo Garcia and Shizhe Chen and Cordelia Schmid},
title = {Towards Generalizable Vision-Language Robotic Manipulation: A Benchmark and LLM-guided 3D Policy},
booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
year = {2025}
}