Learning Accurate Whole-body Throwing with High-frequency Residual Policy and Pullback Tube Acceleration
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866913909209825280 |
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| author | Ma, Yuntao Liu, Yang Qu, Kaixian Hutter, Marco |
| author_facet | Ma, Yuntao Liu, Yang Qu, Kaixian Hutter, Marco |
| contents | Throwing is a fundamental skill that enables robots to manipulate objects in ways that extend beyond the reach of their arms. We present a control framework that combines learning and model-based control for prehensile whole-body throwing with legged mobile manipulators. Our framework consists of three components: a nominal tracking policy for the end-effector, a high-frequency residual policy to enhance tracking accuracy, and an optimization-based module to improve end-effector acceleration control. The proposed controller achieved the average of 0.28 m landing error when throwing at targets located 6 m away. Furthermore, in a comparative study with university students, the system achieved a velocity tracking error of 0.398 m/s and a success rate of 56.8%, hitting small targets randomly placed at distances of 3-5 m while throwing at a specified speed of 6 m/s. In contrast, humans have a success rate of only 15.2%. This work provides an early demonstration of prehensile throwing with quantified accuracy on hardware, contributing to progress in dynamic whole-body manipulation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_16986 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Accurate Whole-body Throwing with High-frequency Residual Policy and Pullback Tube Acceleration Ma, Yuntao Liu, Yang Qu, Kaixian Hutter, Marco Robotics 68T40, 93C85, 70E60 I.2.9; I.2.10; I.2.8 Throwing is a fundamental skill that enables robots to manipulate objects in ways that extend beyond the reach of their arms. We present a control framework that combines learning and model-based control for prehensile whole-body throwing with legged mobile manipulators. Our framework consists of three components: a nominal tracking policy for the end-effector, a high-frequency residual policy to enhance tracking accuracy, and an optimization-based module to improve end-effector acceleration control. The proposed controller achieved the average of 0.28 m landing error when throwing at targets located 6 m away. Furthermore, in a comparative study with university students, the system achieved a velocity tracking error of 0.398 m/s and a success rate of 56.8%, hitting small targets randomly placed at distances of 3-5 m while throwing at a specified speed of 6 m/s. In contrast, humans have a success rate of only 15.2%. This work provides an early demonstration of prehensile throwing with quantified accuracy on hardware, contributing to progress in dynamic whole-body manipulation. |
| title | Learning Accurate Whole-body Throwing with High-frequency Residual Policy and Pullback Tube Acceleration |
| topic | Robotics 68T40, 93C85, 70E60 I.2.9; I.2.10; I.2.8 |
| url | https://arxiv.org/abs/2506.16986 |