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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2407.03245 |
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| _version_ | 1866929551022489600 |
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| author | Peng, Weikun Lv, Jun Zeng, Yuwei Chen, Haonan Zhao, Siheng Sun, Jichen Lu, Cewu Shao, Lin |
| author_facet | Peng, Weikun Lv, Jun Zeng, Yuwei Chen, Haonan Zhao, Siheng Sun, Jichen Lu, Cewu Shao, Lin |
| contents | The tie-knotting task is highly challenging due to the tie's high deformation and long-horizon manipulation actions. This work presents TieBot, a Real-to-Sim-to-Real learning from visual demonstration system for the robots to learn to knot a tie. We introduce the Hierarchical Feature Matching approach to estimate a sequence of tie's meshes from the demonstration video. With these estimated meshes used as subgoals, we first learn a teacher policy using privileged information. Then, we learn a student policy with point cloud observation by imitating teacher policy. Lastly, our pipeline applies learned policy to real-world execution. We demonstrate the effectiveness of TieBot in simulation and the real world. In the real-world experiment, a dual-arm robot successfully knots a tie, achieving 50% success rate among 10 trials. Videos can be found https://tiebots.github.io/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_03245 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TieBot: Learning to Knot a Tie from Visual Demonstration through a Real-to-Sim-to-Real Approach Peng, Weikun Lv, Jun Zeng, Yuwei Chen, Haonan Zhao, Siheng Sun, Jichen Lu, Cewu Shao, Lin Robotics Artificial Intelligence Systems and Control The tie-knotting task is highly challenging due to the tie's high deformation and long-horizon manipulation actions. This work presents TieBot, a Real-to-Sim-to-Real learning from visual demonstration system for the robots to learn to knot a tie. We introduce the Hierarchical Feature Matching approach to estimate a sequence of tie's meshes from the demonstration video. With these estimated meshes used as subgoals, we first learn a teacher policy using privileged information. Then, we learn a student policy with point cloud observation by imitating teacher policy. Lastly, our pipeline applies learned policy to real-world execution. We demonstrate the effectiveness of TieBot in simulation and the real world. In the real-world experiment, a dual-arm robot successfully knots a tie, achieving 50% success rate among 10 trials. Videos can be found https://tiebots.github.io/. |
| title | TieBot: Learning to Knot a Tie from Visual Demonstration through a Real-to-Sim-to-Real Approach |
| topic | Robotics Artificial Intelligence Systems and Control |
| url | https://arxiv.org/abs/2407.03245 |