Traj2Action

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This repo contains training & evaluation code for the paper "Traj2Action: A Co-Denoising Framework for Trajectory-Guided Human-to-Robot Skill Transfer"

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<h1 align="center">Traj2Action: A Co-Denoising Framework for Trajectory-Guided Human-to-Robot Skill Transfer</h1>
 
 <a href="https://www.micdz.cn">Han Zhou</a>1,3,*, <a href="https://scholar.google.com/citations?user=zjN-amcAAAAJ&hl=zh-CN">Jinjin Cao</a>1,2,*, <a href="https://mlyarthur.github.io/">Liyuan Ma</a>1,4,†, <a href="https://xuejifang.github.io/">Xueji Fang</a>1,2, <a href="https://www.westlake.edu.cn/faculty/guojun-qi.html">Guo-jun Qi</a>1,4
 
 1MAPLE Lab, Westlake University, 2Zhejiang University, 3Huazhong University of Science and Technology, 4Institute of Advanced Technology, Westlake Institute for Advanced Study,
 
 *Equal contribution,
 †Corresponding author

In Submission

This repo contains training & evaluation code for the paper "Traj2Action: A Co-Denoising Framework for Trajectory-Guided Human-to-Robot Skill Transfer".

## 🔔 News
* 🔥 [2025-10-02]: We release our dataset.
* 🔥 [2025-09-30]: We release our code, come and check it out!

## Introduction
We present **Traj2Action**, a novel framework that transfers human manipulation skills to robot arms by aligning human hand trajectories with robot end-effector trajectories. Our approach leverages a trajectory alignment model to map human hand movements to robot actions, enabling robots to perform complex manipulation tasks demonstrated by humans. We validate our method on a variety of tasks, showing significant improvements in task success rates and generalization to unseen scenarios.

## How to use

The codebase includes three main modules:
1. **Dataset Conversion and Preparation**: Tools to convert raw data into the LeRobot format, preprocess it, and organize it for efficient loading during training. See `dataset/README.md` for details.
2. **Policy Training and Serving**: A PyTorch-based codebase for learning and serving robot policies, supporting offline training on LeRobot-style datasets, PI0-style policy variants, and real-robot evaluation via a WebSocket policy server. See `policy/README.md` for details.
3. **Robot Control Library**: A library for controlling a Franka Emika Panda robot, including components for robot control, camera management, robot/hand data collection, and task execution. See `robot/README.md` for details.

For a typical workflow, start with dataset conversion and preparation, then proceed to policy training and serving, and finally use the robot control library for real-world robot manipulation tasks. You should follow the instructions in each module's README for setup and usage.
<img src="static/images/software_structure.png" alt="teaser" width="100%" style="max-width: 600px; display: block; margin: 0 auto;"/>

## Main Results
Please visit our [website](https://micdz.github.io/Traj2Action) for the main results and evaluation videos.

## Contact
Han Zhou: [hanzhou04@outlook.com](mailto:hanzhou04@outlook.com)

Jinjin Cao: [caojinjin@westlake.edu.cn](mailto:caojinjin@westlake.edu.cn)

Liyuan Ma: [maliyuan@westlake.edu.cn](mailto:maliyuan@westlake.edu.cn)

## Citation
```tex
@misc{zhou2026traj2actioncodenoisingframeworktrajectoryguided,
      title={Traj2Action: A Co-Denoising Framework for Trajectory-Guided Human-to-Robot Skill Transfer}, 
      author={Han Zhou and Jinjin Cao and Liyuan Ma and Xueji Fang and Guo-jun Qi},
      year={2026},
      eprint={2510.00491},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2510.00491}, 
}
```

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