Incremental Imitation Learning of Context-Dependent Motor Skills

Teaching motor skills to robots through human demonstrations, an approach called "imitation learning", is an alternative to hand coding each new robot behavior. Imitation learning is relatively cheap in terms of time and labor and is a promising route to give robots the necessary functionalities for a widespread use in households, stores, hospitals, etc. However, current imitation learning techniques struggle with a number of challenges that prevent their wide usability. For instance, robots might not be able to accurately reproduce every human demonstration and it is not always clear how robots should generalize a movement to new contexts. This paper addresses those challenges by presenting a method to incrementally teach context-dependent motor skills to robots. The human demonstrates trajectories for different contexts by moving the links of the robot and partially or fully refines those trajectories by disturbing the movements of the robot while it executes the behavior it has learned so far. A joint probability distribution over trajectories and contexts can then be built based on those demonstrations and refinements. Given a new context, the robot computes the most probable trajectory, which can also be refined by the human. The joint probability distribution is incrementally updated with the refined trajectories. We have evaluated our method with experiments in which an elastically actuated robot arm with four degrees of freedom learns how to reach a ball at different positions.