An Analysis of Unified Manipulation with Robot Arms and Dexterous Hands via Optimization-based Motion Synthesis

Abstract

Robot manipulation today generally focuses on motions exclusively with a robot arm or a dexterous hand, but usually not a combination of both. However, complex manipulation tasks can require coordinating arm and hand motions that leverage capabilities of both, much like the coordinated arm and hand motions carried out by humans to perform everyday tasks. In this work, we evaluate unified manipulation with robot arms and dexterous hands, using a motion optimization framework that synthesizes a series of configuration states over the entire manipulation system. We characterize the possible benefits of unifying arm and dexterous hand capabilities within a single model via metrics such as pose accuracy, manipulability, joint-space smoothness, distance to joint-limits, distance to collisions, and more. Several arm-hand combinations are quantitatively compared in simulation on a variety of experiment tasks and performance measures. Our results suggest that combining motions from robot arms and dexterous hands indeed has compelling benefits, highlighting the exciting potential of continued progress in unified arm-hand motion synthesis for robotics applications.