RoboAssembly: Learning Generalizable Furniture Assembly Policy in a Novel Multi-robot Contact-rich Simulation Environment

Abstract

Part assembly is a typical but challenging task in robotics, where robots assemble a set of individual parts into a complete shape. In this paper, we develop a robotic assembly simulation environment for furniture assembly. We formulate the part assembly task as a concrete reinforcement learning problem and propose a pipeline for robots to learn to assemble a diverse set of chairs. Experiments show that when testing with unseen chairs, our approach achieves a success rate of 74.5% under the object-centric setting and 50.0% under the full setting. We adopt an RRT-Connect [1] algorithm as the baseline, which only achieves a success rate of 18.8% after a significantly longer computation time. Supplemental materials and videos are available on our project webpage: https://sites.google.com/view/roboticassembly.

Factory: Fast Contact for Robotic Assembly

“…Furthermore, clearances between rigid parts are typically 0.5-10 mm, substantially greater than real-world clearances. Efforts using PyBullet include [5,57,83,107]. Simulated tasks are again limited to large peg-in-hole insertion and lap-joint mating, with clearances of ∼1 mm.…”