<h1 align="center">RMBench: Memory-Dependent Manipulation Benchmark</h1>

RMBench: Memory-Dependent Robotic Manipulation Benchmark with Insights into Policy Design. <i>Under Review</i>, 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†

🧑🏻‍💻 RMBench Usage

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

1. Installation

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

2. Download Assets

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

3. Download Data

Please run the following command to download all data.

bash script/_download_data.sh

<details> <summary>If you need to collect the data (we actually recommend downloading it directly)</summary>

In RMBench, we always use demo_clean setting.

Running the following command will first search for a random seed for the target collection quantity, and then replay the seed to collect data.

Please strictly follow our tutorial in RoboTwin 2.0 Doc - Collect Data.

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

</details>

4. Run Policies

Mem-0 (ours): See Mem-0 Document
DP: See DP Document
ACT: See ACT Document
Pi 0.5: See Pi 0.5 Document
X-VLA: See X-VLA Document
Other Policies (Pi0, RDT, etc): See Document and See Folder
Configure your policy: See Tutorial Here

👍 Citations

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}
}

🏷️ License

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

RMBench

Install / Use

README