Learning from Demonstration facilitates Human-Robot Collaborative task execution

Abstract

Learning from Demonstration (LfD) is addressed in this work in order to establish a novel framework for Human-Robot Collaborative (HRC) task execution. In this context, a robotic system is trained to perform various actions by observing a human demonstrator. We formulate a latent representation of observed behaviors and associate this representation with the corresponding one for target robotic behaviors. Effectively, a mapping of observed to performed actions is defined, that abstracts action variations and differences between the human and robotic manipulators, and facilitates execution of newly-observed actions. The learned action-behaviors are then employed to accomplish task execution in an HRC scenario. Experimental results obtained regard the successful training of a robotic arm with various action behaviors and its subsequent deployment in HRC task accomplishment. The latter demonstrate the validity and efficacy of the proposed approach in human-robot collaborative setups.