Trajectory Adaptation Of Robot Arms For Head-Pose Dependent Assistive Tasks

Assistive robots promise to increase the autonomy of disabled or elderly people by facilitating the performance of Activities of Daily Living (ADLs). Learning from Demonstration (LfD) has emerged as one of the most promising approaches for teaching robots tasks that are difficult to formalize. LfD learns by requiring the operator to demonstrate one or several times the execution of the task on the given hardware. Unfortunately, many ADLs such as personal grooming, feeding or reading depend on the head pose of the assisted human. Trajectories learned using LfD would become useless or dangerous if applied naively in a situation with a different head pose. In this paper we propose and experimentally validate a method to adapt the trajectories learned using LfD to the current head pose (position and orientation) and movement of the head of the assisted user