RMBench: Memory-Dependent Robotic Manipulation Benchmark with Insights into Policy Design. Under Review, PDF | arXiv | Website | Join our Community 🔥

Tianxing Chen*, Yuran Wang*, Mingleyang Li*, Yan Qin*, Hao Shi, Zixuan Li, Yifan Hu, Yingsheng Zhang, Kaixuan Wang, Yue Chen, Hongcheng Wang, Renjing Xu, Ruihai Wu, Yao Mu, Yaodong Yang, Hao Dong†, Ping Luo†

This project is built upon RoboTwin 2.0, and you can seamlessly transfer your policy code between the two projects.

First, prepare a conda environment.

conda create -n RMBench python=3.10 -y
conda activate RMBench

RMBench Repo: https://github.com/RoboTwin-Platform/RMBench

git clone https://github.com/RoboTwin-Platform/RMBench.git

Then, run script/_install.sh to install basic conda envs and CuRobo:

bash script/_install.sh

To download the assets, run the following command. If you encounter any rate-limit issues, please log in to your Hugging Face account by running huggingface-cli login:

bash script/_download_assets.sh

Please run the following command to download all data.

bash script/_download_data.sh

bash collect_data.sh ${task_name} ${task_config} ${gpu_id}
# Example: bash collect_data.sh cover_blocks demo_clean 0

1. Mem-0 (ours): See Mem-0 Document
2. DP: See DP Document
3. ACT: See ACT Document
4. Pi 0.5: See Pi 0.5 Document
5. X-VLA: See X-VLA Document
6. Other Policies (Pi0, RDT, etc): See Document and See Folder
7. Configure your policy: See Tutorial Here

If you find our work useful, please consider citing:

@article{chen2026rmbench,
  title={RMBench: Memory-Dependent Robotic Manipulation Benchmark with Insights into Policy Design},
  author={Chen, Tianxing and Wang, Yuran and Li, Mingleyang and Qin, Yan and Shi, Hao and Li, Zixuan and Hu, Yifan and Zhang, Yingsheng and Wang, Kaixuan and Chen, Yue and others},
  journal={arXiv preprint arXiv:2603.01229},
  year={2026}
}

This repository is released under the MIT license. See LICENSE for additional details.