ASAP: Automated Sequence Planning for Complex Robotic Assembly with Physical Feasibility
This addresses the challenge of robotic assembly automation for complex products, offering a novel method for generating realistic sequences, though it is incremental in combining physics with search and learning.
The paper tackles the problem of automatically planning physically feasible assembly sequences for complex products, and presents ASAP, a physics-based approach that accounts for gravity and stability, achieving superior performance on a large dataset of hundreds of assemblies.
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