ASAP: 物理的実現可能性を考慮した複雑なロボット組立のための自動シーケンス計画
ASAP: Automated Sequence Planning for Complex Robotic Assembly with Physical Feasibility
重力を考慮し各部品が安定して保持される組立順序を、木探索とGNNで効率的に自動計画する手法を提案し、多数の製品組立データと実機で有効性を示した。
著者: Yunsheng Tian, Karl D. D. Willis, Bassel Al Omari, Jieliang Luo, Pingchuan Ma, Yichen Li, Farhad Javid, Edward Gu, Joshua Jacob, Shinjiro Sueda, Hui Li, Sachin Chitta, Wojciech Matusik
分類: cs.RO, cs.AI, cs.GR
原文アブストラクト
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
