Evaluating intent-expressive robot arm motion

Abstract

Planning effective arm motions is integral to manipulation tasks. In general, motion synthesis methods have focused on functional objectives, such as minimizing time and maximizing efficiency. However, recent work in human-robot collaboration suggests that choices in motion design can influence collaboration performance and quality. Some motion designs are easier than others for human observers to interpret. In this paper, we explore the tradeoffs in robot arm movements designed to be observed by people. Through a series of human-subjects experiments, we compare collaboration performance between several motion-synthesis methods explored by prior work. We find that a number of factors, including the design of the robot arm and metric for success, affect the relative merits of different approaches.