Few-shot Sim2Real Based on High Fidelity Rendering with Force Feedback Teleoperation
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866913811718471680 |
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| author | Zou, Yanwen Huang, Junda Liang, Boyuan Guo, Honghao Liu, Zhengyang Ma, Xin Zhou, Jianshu Tomizuka, Masayoshi |
| author_facet | Zou, Yanwen Huang, Junda Liang, Boyuan Guo, Honghao Liu, Zhengyang Ma, Xin Zhou, Jianshu Tomizuka, Masayoshi |
| contents | Teleoperation offers a promising approach to robotic data collection and human-robot interaction. However, existing teleoperation methods for data collection are still limited by efficiency constraints in time and space, and the pipeline for simulation-based data collection remains unclear. The problem is how to enhance task performance while minimizing reliance on real-world data. To address this challenge, we propose a teleoperation pipeline for collecting robotic manipulation data in simulation and training a few-shot sim-to-real visual-motor policy. Force feedback devices are integrated into the teleoperation system to provide precise end-effector gripping force feedback. Experiments across various manipulation tasks demonstrate that force feedback significantly improves both success rates and execution efficiency, particularly in simulation. Furthermore, experiments with different levels of visual rendering quality reveal that enhanced visual realism in simulation substantially boosts task performance while reducing the need for real-world data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_01301 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Few-shot Sim2Real Based on High Fidelity Rendering with Force Feedback Teleoperation Zou, Yanwen Huang, Junda Liang, Boyuan Guo, Honghao Liu, Zhengyang Ma, Xin Zhou, Jianshu Tomizuka, Masayoshi Robotics Teleoperation offers a promising approach to robotic data collection and human-robot interaction. However, existing teleoperation methods for data collection are still limited by efficiency constraints in time and space, and the pipeline for simulation-based data collection remains unclear. The problem is how to enhance task performance while minimizing reliance on real-world data. To address this challenge, we propose a teleoperation pipeline for collecting robotic manipulation data in simulation and training a few-shot sim-to-real visual-motor policy. Force feedback devices are integrated into the teleoperation system to provide precise end-effector gripping force feedback. Experiments across various manipulation tasks demonstrate that force feedback significantly improves both success rates and execution efficiency, particularly in simulation. Furthermore, experiments with different levels of visual rendering quality reveal that enhanced visual realism in simulation substantially boosts task performance while reducing the need for real-world data. |
| title | Few-shot Sim2Real Based on High Fidelity Rendering with Force Feedback Teleoperation |
| topic | Robotics |
| url | https://arxiv.org/abs/2503.01301 |