File size: 3,281 Bytes
8e139d9 4a92b2c 8e139d9 bae1e7e aa19799 bae1e7e 5e540d9 aa19799 8260e37 aa19799 bae1e7e 5e540d9 bae1e7e aa19799 5e540d9 aa19799 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 |
---
license: mit
task_categories:
- robotics
language:
- en
tags:
- code
pretty_name: GEMBench dataset
size_categories:
- 10K<n<100K
---
# Dataset Card for GEMBench dataset
💎 **GE**neralizable vision-language robotic **M**anipulation **Bench**mark 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
1. Create and activate your conda environment:
```bash
conda create -n gembench python==3.10
conda activate gembench
```
2. Install RLBench
Download CoppeliaSim (see instructions [here](https://github.com/stepjam/PyRep?tab=readme-ov-file#install))
```bash
# 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:
```bash
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
```bash
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](https://arxiv.org/abs/2410.01345):
**BibTeX:**
```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}
}
```
## Contact
[Ricardo Garcia-Pinel](mailto:[email protected]) |