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Part assembly is a typical but challenging task in robotics, where robotsassemble a set of individual parts into a complete shape. In this paper, wedevelop a robotic assembly simulation environment for furniture assembly. Weformulate the part assembly task as a concrete reinforcement learning problemand 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 asuccess rate of 74.5% under the object-centric setting and 50.0% under the fullsetting. We adopt an RRT-Connect algorithm as the baseline, which only achievesa success rate of 18.8% after a significantly longer computation time.Supplemental materials and videos are available on our project webpage.

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