  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.
