We study real-time collaborative robot (cobot) handling, where the cobot maneuvers a workpiece under human commands. This is useful when it is risky for humans to directly handle the workpiece. However, it is hard to make the cobot both easy to command and flexible in possible operations. In this work, we propose a Real-Time Collaborative Robot Handling (RTCoHand) framework that allows the control of cobot via user-customized dynamic gestures. This is hard due to variations among users, human motion uncertainties, and noisy human input. We model the task as a probabilistic generative process, referred to as Conditional Collaborative Handling Process (CCHP), and learn from human-human collaboration. We thoroughly evaluate the adaptability and robustness of CCHP and apply our approach to a real-time cobot handling task with Kinova Gen3 robot arm. We achieve seamless human-robot collaboration with both experienced and new users. Compared to classical controllers, RTCoHand allows significantly more complex maneuvers and lower user cognitive burden. It also eliminates the need for trial-and-error, rendering it advantageous in safety-critical tasks.

翻译：我们研究实时协作机器人(cobot) 操作, 使cobot 在人类指令下操控一个工作器件。 当人类直接处理工作器有风险时, 这样做是有用的。 但是, 很难让 cobot 在可能的操作中容易命令和灵活。 在这项工作中, 我们提出一个实时协作机器人处理( RTCoHand) 框架, 通过用户定制的动态手势来控制 cobot 。 这很难做到。 与古典控制器相比, RTCoHand 允许使用更复杂得多的操控器和较低的用户认知负担。 我们还消除了对试验和稳定任务的需求, 将它置于最安全的位置上。