Learning force-based assembly skills from human demonstration for execution in unstructured environments

Robots have been used successfully in structured settings, where the environment is controlled; this research is inspired by the vision of robots moving beyond the structured, controlled settings. The work focuses on the problem of learning low-level force-based assembly skills from human demonstration. To avoid position dependencies, force-based discrete states are used to describe qualitatively how contact is being made with the environment. Sensorimotor skills are modeled using a hybrid control model, which provides a mechanism for combining continuous low-level force control with higher level discrete event control. A change in qualitative, discrete state constitutes an event and triggers a new control command to the robot. In this way, the skill execution is not dependent on absolute position but rather responds to changes in the force-based qualitative state. Experimental results are presented which validate the approach and show how skill acquisition can be accomplished even with an imperfect demonstration