Neural Configuration-Space Barriers for Manipulation Planning and Control
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866913153496907776 |
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| author | Long, Kehan Lee, Ki Myung Brian Raicevic, Nikola Attasseri, Niyas Leok, Melvin Atanasov, Nikolay |
| author_facet | Long, Kehan Lee, Ki Myung Brian Raicevic, Nikola Attasseri, Niyas Leok, Melvin Atanasov, Nikolay |
| contents | Planning and control for high-dimensional robot manipulators in cluttered dynamic environments require computational efficiency and robust safety guarantees. Inspired by recent advances in learning configuration-space distance functions (CDFs) as representations of robot bodies, we propose a unified approach for motion planning and control that formulates safety constraints as CDF barriers. A CDF barrier approximates the local free configuration space, substantially reducing the number of collision-checking operations during motion planning. However, learning a CDF barrier with a neural network and relying on online sensor observations introduces uncertainties that must be considered during control synthesis. To address this, we develop a distributionally robust CDF barrier formulation for control that accounts for modeling errors and sensor noise without assuming a known underlying distribution. Simulations and hardware experiments on a UFactory xArm6 manipulator show that our neural CDF barrier formulation enables efficient planning and robust safe control in cluttered and dynamic environments, relying only on onboard point-cloud observations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_04929 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neural Configuration-Space Barriers for Manipulation Planning and Control Long, Kehan Lee, Ki Myung Brian Raicevic, Nikola Attasseri, Niyas Leok, Melvin Atanasov, Nikolay Robotics Machine Learning Systems and Control Planning and control for high-dimensional robot manipulators in cluttered dynamic environments require computational efficiency and robust safety guarantees. Inspired by recent advances in learning configuration-space distance functions (CDFs) as representations of robot bodies, we propose a unified approach for motion planning and control that formulates safety constraints as CDF barriers. A CDF barrier approximates the local free configuration space, substantially reducing the number of collision-checking operations during motion planning. However, learning a CDF barrier with a neural network and relying on online sensor observations introduces uncertainties that must be considered during control synthesis. To address this, we develop a distributionally robust CDF barrier formulation for control that accounts for modeling errors and sensor noise without assuming a known underlying distribution. Simulations and hardware experiments on a UFactory xArm6 manipulator show that our neural CDF barrier formulation enables efficient planning and robust safe control in cluttered and dynamic environments, relying only on onboard point-cloud observations. |
| title | Neural Configuration-Space Barriers for Manipulation Planning and Control |
| topic | Robotics Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2503.04929 |