A probabilistic approach for learning and adapting shared control skills with the human in the loop

Abstract

Assistive robots promise to be of great help to wheelchair users with motor impairments, for example for activities of daily living. Using shared control to provide task-specific assistance -- for instance with the Shared Control Templates (SCT) framework -- facilitates user control, even with low-dimensional input signals. However, designing SCTs is a laborious task requiring robotic expertise. To facilitate their design, we propose a method to learn one of their core components -- active constraints -- from demonstrated end-effector trajectories. We use a probabilistic model, Kernelized Movement Primitives, which additionally allows adaptation from user commands to improve the shared control skills, during both design and execution. We demonstrate that the SCTs so acquired can be successfully used to pick up an object, as well as adjusted for new environmental constraints, with our assistive robot EDAN.