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--- |
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license: mit |
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task_categories: |
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- robotics |
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tags: |
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- tactile |
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--- |
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# 📦 FreeTacman |
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## Robot-free Visuo-Tactile Data Collection System for Contact-rich Manipulation |
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## 🎯 Overview |
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This dataset supports the paper **[FreeTacman: Robot-free Visuo-Tactile Data Collection System for Contact-rich Manipulation](http://arxiv.org/abs/2506.01941)**. It contains a large-scale, high-precision visuo-tactile manipulation dataset with over 3000k visuo-tactile image pairs, more than 10k trajectories across 50 tasks. |
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Please refer to our 🚀 [Website](http://opendrivelab.com/freetacman) | 📄 [Paper](http://arxiv.org/abs/2506.01941) | 💻 [Code](https://github.com/OpenDriveLab/FreeTacMan) | 🛠️ [Hardware Guide](https://docs.google.com/document/d/1Hhi2stn_goXUHdYi7461w10AJbzQDC0fdYaSxMdMVXM/edit?addon_store&tab=t.0#heading=h.rl14j3i7oz0t) | 📺 [Video](https://opendrivelab.github.io/FreeTacMan/landing/FreeTacMan_demo_video.mp4) | 🌐 [X](https://x.com/OpenDriveLab/status/1930234855729836112) for more details. |
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## 🔬 Potential Applications |
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The FreeTacman dataset enables diverse research directions in visuo-tactile learning and manipulation: |
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- **System Reproduction**: For researchers interested in hardware implementation, you can reproduce FreeTacMan from scratch using our 🛠️ [Hardware Guide](https://docs.google.com/document/d/1Hhi2stn_goXUHdYi7461w10AJbzQDC0fdYaSxMdMVXM/edit?addon_store&tab=t.0#heading=h.rl14j3i7oz0t) and 💻 [Code](https://github.com/OpenDriveLab/FreeTacMan). |
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- **Multimodal Imitation Learning**: Transfer to other LED-based tactile sensors (such as GelSight) for developing robust multimodal imitation learning frameworks. |
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- **Tactile-aware Grasping**: Utilize the dataset for pre-training tactile representation models and developing tactile-aware reasoning systems. |
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- **Simulation-to-Real Transfer**: Leverage the dynamic tactile interaction sequences to enhance tactile simulation fidelity, significantly reducing the sim2real gap. |
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## 📂 Dataset Structure |
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The dataset is organized into 50 task categories, each containing: |
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- **Video files**: Synchronized video recordings from the wrist-mounted and visuo-tactile cameras for each demonstration |
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- **Trajectory files**: Detailed tracking data for tool center point pose and gripper distance |
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## 🧾 Data Format |
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### Video Files |
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- **Format**: MP4 |
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- **Views**: Wrist-mounted camera and visuo-tactile camera perspectives per demonstration |
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### Trajectory Files |
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Each trajectory file contains the following data columns: |
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#### Timestamp |
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- `timestamp` - Unix Timestamp |
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#### Tool Center Point (TCP) Data |
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- `TCP_pos_x`, `TCP_pos_y`, `TCP_pos_z` - TCP position |
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- `TCP_euler_x`, `TCP_euler_y`, `TCP_euler_z` - TCP orientation (euler angles) |
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- `quat_w`, `quat_x`, `quat_y`, `quat_z` - TCP orientation (quaternion representation) |
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#### Gripper Data |
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- `gripper_distance` - Gripper opening distance |
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## 📝 Citation |
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If you use this dataset in your research, please cite: |
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```bibtex |
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@article{wu2025freetacman, |
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title={Freetacman: Robot-free visuo-tactile data collection system for contact-rich manipulation}, |
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author={Wu, Longyan and Yu, Checheng and Ren, Jieji and Chen, Li and Jiang, Yufei and Huang, Ran and Gu, Guoying and Li, Hongyang}, |
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journal={arXiv preprint arXiv:2506.01941}, |
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year={2025} |
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} |
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``` |
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## 💼 License |
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This dataset is released under the MIT License. See LICENSE file for details. |
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## 📧 Contact |
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For questions or issues regarding the dataset, please contact: Longyan Wu (im.longyanwu@gmail.com). |
