Learn and Transfer Knowledge of Preferred Assistance Strategies in Semi-autonomous Telemanipulation
This work addresses the problem of ambiguous human-robot interaction in semi-autonomous telemanipulation for operators, though it is incremental as it builds on existing assistance methods.
The paper tackled the challenge of robots providing intuitive assistance in telemanipulation by developing a preference-aware learning approach that learns human assistance preferences and transfers knowledge across different robot hand structures, resulting in effective learning and reduced training effort as demonstrated in experiments with cup manipulation tasks.
Enabling robots to provide effective assistance yet still accommodating the operator's commands for telemanipulation of an object is very challenging because robot's assistive action is not always intuitive for human operators and human behaviors and preferences are sometimes ambiguous for the robot to interpret. Although various assistance approaches are being developed to improve the control quality from different optimization perspectives, the problem still remains in determining the appropriate approach that satisfies the fine motion constraints for the telemanipulation task and preference of the operator. To address these problems, we developed a novel preference-aware assistance knowledge learning approach. An assistance preference model learns what assistance is preferred by a human, and a stagewise model updating method ensures the learning stability while dealing with the ambiguity of human preference data. Such a preference-aware assistance knowledge enables a teleoperated robot hand to provide more active yet preferred assistance toward manipulation success. We also developed knowledge transfer methods to transfer the preference knowledge across different robot hand structures to avoid extensive robot-specific training. Experiments to telemanipulate a 3-finger hand and 2-finger hand, respectively, to use, move, and hand over a cup have been conducted. Results demonstrated that the methods enabled the robots to effectively learn the preference knowledge and allowed knowledge transfer between robots with less training effort.