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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2309.16909 |
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| _version_ | 1866909123068559360 |
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| author | Tian, Yunsheng Willis, Karl D. D. Omari, Bassel Al Luo, Jieliang Ma, Pingchuan Li, Yichen Javid, Farhad Gu, Edward Jacob, Joshua Sueda, Shinjiro Li, Hui Chitta, Sachin Matusik, Wojciech |
| author_facet | Tian, Yunsheng Willis, Karl D. D. Omari, Bassel Al Luo, Jieliang Ma, Pingchuan Li, Yichen Javid, Farhad Gu, Edward Jacob, Joshua Sueda, Shinjiro Li, Hui Chitta, Sachin Matusik, Wojciech |
| contents | The automated assembly of complex products requires a system that can automatically plan a physically feasible sequence of actions for assembling many parts together. In this paper, we present ASAP, a physics-based planning approach for automatically generating such a sequence for general-shaped assemblies. ASAP accounts for gravity to design a sequence where each sub-assembly is physically stable with a limited number of parts being held and a support surface. We apply efficient tree search algorithms to reduce the combinatorial complexity of determining such an assembly sequence. The search can be guided by either geometric heuristics or graph neural networks trained on data with simulation labels. Finally, we show the superior performance of ASAP at generating physically realistic assembly sequence plans on a large dataset of hundreds of complex product assemblies. We further demonstrate the applicability of ASAP on both simulation and real-world robotic setups. Project website: asap.csail.mit.edu |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_16909 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | ASAP: Automated Sequence Planning for Complex Robotic Assembly with Physical Feasibility Tian, Yunsheng Willis, Karl D. D. Omari, Bassel Al Luo, Jieliang Ma, Pingchuan Li, Yichen Javid, Farhad Gu, Edward Jacob, Joshua Sueda, Shinjiro Li, Hui Chitta, Sachin Matusik, Wojciech Robotics Artificial Intelligence Graphics The automated assembly of complex products requires a system that can automatically plan a physically feasible sequence of actions for assembling many parts together. In this paper, we present ASAP, a physics-based planning approach for automatically generating such a sequence for general-shaped assemblies. ASAP accounts for gravity to design a sequence where each sub-assembly is physically stable with a limited number of parts being held and a support surface. We apply efficient tree search algorithms to reduce the combinatorial complexity of determining such an assembly sequence. The search can be guided by either geometric heuristics or graph neural networks trained on data with simulation labels. Finally, we show the superior performance of ASAP at generating physically realistic assembly sequence plans on a large dataset of hundreds of complex product assemblies. We further demonstrate the applicability of ASAP on both simulation and real-world robotic setups. Project website: asap.csail.mit.edu |
| title | ASAP: Automated Sequence Planning for Complex Robotic Assembly with Physical Feasibility |
| topic | Robotics Artificial Intelligence Graphics |
| url | https://arxiv.org/abs/2309.16909 |