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We study real-time collaborative robot (cobot) handling, where the cobotmaneuvers a workpiece under human commands. This is useful when it is risky forhumans to directly handle the workpiece. However, it is hard to make the cobotboth easy to command and flexible in possible operations. In this work, wepropose a Real-Time Collaborative Robot Handling (RTCoHand) framework thatallows the control of cobot via user-customized dynamic gestures. This is harddue to variations among users, human motion uncertainties, and noisy humaninput. We model the task as a probabilistic generative process, referred to asConditional Collaborative Handling Process (CCHP), and learn from human-humancollaboration. We thoroughly evaluate the adaptability and robustness of CCHPand apply our approach to a real-time cobot handling task with Kinova Gen3robot arm. We achieve seamless human-robot collaboration with both experiencedand new users. Compared to classical controllers, RTCoHand allows significantlymore complex maneuvers and lower user cognitive burden. It also eliminates theneed for trial-and-error, rendering it advantageous in safety-critical tasks.

Learn from Human Teams: a Probabilistic Solution to Real-Time Collaborative Robot Handling with Dynamic Gesture Commands