This paper studies real-time collaborative robot (cobot) handling, where the cobot maneuvers an object under human dynamic gesture commands. Enabling dynamic gesture commands is useful when the human needs to avoid direct contact with the robot or the object handled by the robot. However, the key challenge lies in the heterogeneity in human behaviors and the stochasticity in the perception of dynamic gestures, which requires the robot handling policy to be adaptable and robust. To address these challenges, we introduce Conditional Collaborative Handling Process (CCHP) to encode a contextaware cobot handling policy and a procedure to learn such policy from human-human collaboration. We thoroughly evaluate the adaptability and robustness of CCHP and apply our approach to a real-time cobot assembly task with Kinova Gen3 robot arm. Results show that our method leads to significantly less human effort and smoother human-robot collaboration than state-of-the-art rule-based approach even with first-time users.
翻译:本文研究实时协作机器人(cobot)处理,其中cobot在人类动态手势命令下操纵物体。启用动态手势命令在人类需要避免与机器人或机器人操纵的物体直接接触时非常有用。然而,关键的挑战在于人类行为的异质性和动态手势感知的随机性,这要求机器人操纵策略具有适应性和稳健性。为了解决这些问题,我们引入了一种有条件的协作处理过程(CCHP),用于编码适应场景的cobot操纵策略和从人-人协作中学习这种策略的过程。我们深入评估了CCHP的适应性和稳健性,并将我们的方法应用于使用Kinova Gen3机械臂进行实时cobot组装任务。结果表明,相比最先进的基于规则的方法,我们的方法即使在初次使用者的情况下也能导致人力成本更低、人机协作更加平稳。