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--- |
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license: mit |
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task_categories: |
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- robotics |
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language: |
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- en |
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tags: |
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- code |
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pretty_name: GEMBench dataset |
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size_categories: |
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- 10K<n<100K |
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--- |
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# Dataset Card for GEMBench dataset |
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💎 **GE**neralizable vision-language robotic **M**anipulation **Bench**mark Dataset |
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A benchmark to systematically evaluate generalization capabilities of vision-and-language robotic manipulation policies. Built upon the RLBench simulator. |
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💻 **GEMBench Project Webpage:** https://www.di.ens.fr/willow/research/gembench/ |
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📈 **Leaderboard:** https://paperswithcode.com/sota/robot-manipulation-generalization-on-gembench |
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## Dataset Structure |
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Dataset structure is as follows: |
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``` |
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- gembench |
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- train_dataset |
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- microsteps: 567M, initial configurations for each episode |
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- keysteps_bbox: 160G, extracted keysteps data |
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- keysteps_bbox_pcd: (used to train 3D-LOTUS) |
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- voxel1cm: 10G, processed point clouds |
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- instr_embeds_clip.npy: instructions encoded by CLIP text encoder |
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- motion_keysteps_bbox_pcd: (used to train 3D-LOTUS++ motion planner) |
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- voxel1cm: 2.8G, processed point clouds |
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- action_embeds_clip.npy: action names encoded by CLIP text encoder |
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- val_dataset |
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- microsteps: 110M, initial configurations for each episode |
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- keysteps_bbox_pcd: |
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- voxel1cm: 941M, processed point clouds |
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- test_dataset |
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- microsteps: 2.2G, initial configurations for each episode |
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``` |
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## 🛠️ Benchmark Installation |
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1. Create and activate your conda environment: |
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```bash |
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conda create -n gembench python==3.10 |
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conda activate gembench |
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``` |
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2. Install RLBench |
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Download CoppeliaSim (see instructions [here](https://github.com/stepjam/PyRep?tab=readme-ov-file#install)) |
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```bash |
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# change the version if necessary |
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wget https://www.coppeliarobotics.com/files/V4_1_0/CoppeliaSim_Edu_V4_1_0_Ubuntu20_04.tar.xz |
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tar -xvf CoppeliaSim_Edu_V4_1_0_Ubuntu20_04.tar.xz |
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``` |
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Add the following to your ~/.bashrc file: |
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```bash |
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export COPPELIASIM_ROOT=$(pwd)/CoppeliaSim_Edu_V4_1_0_Ubuntu20_04 |
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export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$COPPELIASIM_ROOT |
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export QT_QPA_PLATFORM_PLUGIN_PATH=$COPPELIASIM_ROOT |
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``` |
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Install Pyrep and RLBench |
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```bash |
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git clone https://github.com/cshizhe/PyRep.git |
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cd PyRep |
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pip install -r requirements.txt |
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pip install . |
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cd .. |
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# Our modified version of RLBench to support new tasks in GemBench |
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git clone https://github.com/rjgpinel/RLBench |
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cd RLBench |
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pip install -r requirements.txt |
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pip install . |
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cd .. |
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``` |
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## Evaluation |
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Please, check 3D-LOTUS++ official code repository for evaluation: |
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https://github.com/vlc-robot/robot-3dlotus?tab=readme-ov-file#evaluation |
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## Citation |
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If you use our GemBench benchmark or find our code helpful, please kindly cite our [work](https://arxiv.org/abs/2410.01345): |
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**BibTeX:** |
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```bibtex |
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@inproceedings{garcia25gembench, |
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author = {Ricardo Garcia and Shizhe Chen and Cordelia Schmid}, |
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title = {Towards Generalizable Vision-Language Robotic Manipulation: A Benchmark and LLM-guided 3D Policy}, |
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booktitle = {IEEE International Conference on Robotics and Automation (ICRA)}, |
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year = {2025} |
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} |
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``` |
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## Contact |
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[Ricardo Garcia-Pinel](mailto:[email protected]) |
