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）操作问题，即机器人根据人体动态手势指令操控物体。当操作者需避免直接接触机器人或其操控的物体时，启用动态手势指令具有实用价值。然而，关键挑战在于人类行为的异质性与动态手势感知的随机性，这要求机器人操作策略兼具适应性与鲁棒性。针对上述挑战，我们提出条件式协作操作过程（CCHP）以编码情境感知的协作机器人操作策略，并建立从人类协作中学习该策略的流程。我们全面评估了CCHP的适应性与鲁棒性，并将该方法应用于基于Kinova Gen3机械臂的实时协作装配任务。结果表明，即使对于首次使用者，本方法相较于现有基于规则的先进方法，能显著降低人力消耗并实现更流畅的人机协作。