An experience-driven robotic assistant acquiring human knowledge to improve haptic cooperation

摘要

Physical cooperation with humans greatly enhances the capabilities of robotic systems when leaving standardized industrial settings. Our novel cognition-enabled control framework presented in this paper enables a robotic assistant to enrich its own experience by acquisition of human task knowledge during joint manipulation. Our robot incrementally learns semantic task structures during joint task execution using hierarchically clustered Hidden Markov Models. A semantic labeling of recognized task segments is acquired from the human partner through speech. After a small number of repetitions, the robot uses an anticipated task progress to generate a feed-forward set point for an admittance feedback control scheme. This paper describes the framework and its implementation on a mobile bi-manual platform. The evolution of the robot's task knowledge is presented and discussed. Finally, the cooperation quality is measured in terms of the robot's task contribution.