Parts assembly planning under uncertainty with simulation-aided physical reasoning

Parts assembly, in a broad sense, is to make multiple objects to be in specific relative poses in contact with each other. One of the major reasons that make it difficult is uncertainty. Because parts assembly involves physical contact between objects, it requires higher precision than other manipulation tasks like collision avoidance. The key idea of this paper is to use simulation-aided physical reasoning while planning with the goal of finding a robust motion plan for parts assembly. Specifically, in the proposed approach, a) uncertainty between object poses is represented as a distribution of particles, b) the motion planner estimates the transition of particles for unit actions (motion primitives) through physics-based simulation, and c) the performance of the planner is sped up using Multi-Heuristic A* (MHA*) search that utilizes multiple inadmissible heuristics that lead to fast uncertainty reduction. To demonstrate the benefits of our framework, motion planning and physical robot experiments for several parts assembly tasks are provided.