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retargeting

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Repository for Paper "Analyzing Key Objectives in Human-to-Robot Retargeting for Dexterous Manipulation"

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# Analyzing Key Objectives in Human-to-Robot Retargeting for Dexterous Manipulation

<p align="center">
  <a href="https://mingrui-yu.github.io/retargeting/">Project Website</a>
  &middot;
  <a href="https://arxiv.org/abs/2506.09384">arXiv</a>
</p>

This repository contains the code for the paper "Analyzing Key Objectives in Human-to-Robot Retargeting for Dexterous Manipulation".

It provides:

- A Python core package for human-to-robot dexterous-hand retargeting.
- Config-driven robot, asset, and retargeting setup.
- Offline replay tooling for quick inspection without ROS or hardware.
- Optional ROS2/RViz, live input, and real robot adapters.

## What's New

**2026-07-13** — The codebase has been comprehensively **reorganized** with clearer boundaries between the retargeting core, runtime adapters, configuration, and applications. The new structure also makes quick offline replay easier to discover and run.

We welcome reproductions of this work and use of this codebase as a baseline. Please open an issue with any questions; we will address them and update the repository promptly.

## Install

The default setup supports offline retargeting, replay, and visualized MuJoCo teleoperation without ROS or robot hardware.

```bash
git clone --recurse-submodules https://github.com/Mingrui-Yu/retargeting.git
cd retargeting

conda create -n retargeting -c conda-forge python=3.10.12 pinocchio
conda activate retargeting

pip install -e ".[replay,mujoco-web]"
```

For an existing clone, initialize the pinned `mr_utils` submodule before installation:

```bash
git submodule update --init --recursive
```

PyTorch is required by optimizer paths. Install the build matching your CUDA environment from the [official PyTorch instructions](https://pytorch.org/); it is not pinned because the correct wheel depends on the local CUDA runtime.

## Quickstart: Offline Replay

From the repository root, retarget the bundled hand trajectory and open the result in the Viser Web viewer:

```bash
python -m retargeting_apps.main app=offline_retarget end=200 run_name=quickstart_leap \
  post.visualize.enabled=true
```

The terminal prints the viewer address. To open the saved result again without rerunning retargeting:

```bash
python -m retargeting_apps.main app=replay run_name=quickstart_leap
```

Optionally compute benchmark statistics and plots from the same result:

```bash
python -m retargeting_apps.main app=benchmark run_name=quickstart_leap
```

## Teleoperation Flow

Run the bundled raw hand trajectory through the full teleoperation flow and visualize the robot in MuJoCo:

```bash
python -m retargeting_apps.main app=teleop_exe teleoperation_modes=offline_mujoco \
  viewer.enabled=true teleoperation_mode.pipeline.realtime=true input.loop=true
```

This runs the same execution path used by live teleoperation:

```text
offline hand input -> observation mapping -> retargeting -> MuJoCo backend -> Web viewer
```

Open the viewer address printed in the terminal. Press `Ctrl+C` to stop playback.

## Citation

```bibtex
@article{xin2026analyzing,
  title={Analyzing Key Objectives in Human-to-Robot Retargeting for Dexterous Manipulation},
  author={Xin, Chendong and Yu, Mingrui and Jiang, Yongpeng and Zhang, Zhefeng and Li, Xiang},
  journal={IEEE Robotics and Automation Practice},
  volume={1},
  pages={29--34},
  year={2026},
  doi={10.1109/RAP.2026.3656110}
}
```

## Contact

For questions, contact Mingrui Yu at [mingruiyu98@gmail.com](mailto:mingruiyu98@gmail.com).

retargeting

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