Closed-loop multi-step planning with innate physics knowledge
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866910703217016832 |
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| author | Lafratta, Giulia Porr, Bernd Chandler, Christopher Miller, Alice |
| author_facet | Lafratta, Giulia Porr, Bernd Chandler, Christopher Miller, Alice |
| contents | We present a hierarchical framework to solve robot planning as an input control problem. At the lowest level are temporary closed control loops, ("tasks"), each representing a behaviour, contingent on a specific sensory input and therefore temporary. At the highest level, a supervising "Configurator" directs task creation and termination. Here resides "core" knowledge as a physics engine, where sequences of tasks can be simulated. The Configurator encodes and interprets simulation results,based on which it can choose a sequence of tasks as a plan. We implement this framework on a real robot and test it in an overtaking scenario as proof-of-concept. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_11510 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Closed-loop multi-step planning with innate physics knowledge Lafratta, Giulia Porr, Bernd Chandler, Christopher Miller, Alice Robotics Artificial Intelligence Emerging Technologies Systems and Control We present a hierarchical framework to solve robot planning as an input control problem. At the lowest level are temporary closed control loops, ("tasks"), each representing a behaviour, contingent on a specific sensory input and therefore temporary. At the highest level, a supervising "Configurator" directs task creation and termination. Here resides "core" knowledge as a physics engine, where sequences of tasks can be simulated. The Configurator encodes and interprets simulation results,based on which it can choose a sequence of tasks as a plan. We implement this framework on a real robot and test it in an overtaking scenario as proof-of-concept. |
| title | Closed-loop multi-step planning with innate physics knowledge |
| topic | Robotics Artificial Intelligence Emerging Technologies Systems and Control |
| url | https://arxiv.org/abs/2411.11510 |