ASAP: Automated Sequence Planning for Complex Robotic Assembly with Physical Feasibility

TL;DR

ASAP is a physics-based planning system that automatically generates physically feasible assembly sequences for complex, general-shaped products, utilizing tree search algorithms guided by heuristics or neural networks, applicable in simulation and real-world robotics.

Contribution

This paper introduces ASAP, a novel physics-based planning approach that efficiently generates assembly sequences considering physical stability, using advanced search and learning techniques.

Findings

ASAP outperforms existing methods in generating realistic assembly sequences.

The approach is effective on large datasets of complex assemblies.

ASAP is applicable to both simulation and real-world robotic assembly.

Abstract

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