A Haptic-enabled Robot That Supports Play By Learning A Surface From Motion Trajectories

Haptic-enabled teleoperated robots can help children with physical disabilities to reach toys by applying haptic guidance towards their toys, thus compensating for their limitations in reaching and manipulating objects. In this article we preliminarily tested a learning from demonstration (LfD) approach, where a robotic system learnt the surface that best approximated to all motion trajectories demonstrated by the participants while playing a whack-a-mole game. The end-goal of the system is for therapists or parents to demonstrate to it how to play a game, and then be used by children with physical disabilities. In this study, four adults without disabilities participated, to identify aspects that will be necessary to improve before conducting trials with children. During the demonstration phase, participants played the game in normal teleoperation, assuming the role of the therapist/parent. Then, the surface was modeled using a neural network. Participants played the game without and with the haptic guidance. The movements of the robotic system were mirrored to induce errors in movements, and thus require the guidance. Participants spent more time, moved the robot longer distances, and had jerkier movements when they played the game with the guidance than without it. Possible reasons were discussed, and several solutions were proposed to improve the system. The main contribution of this paper was the learning of a surface instead of learning a single motion trajectory.