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Learning and adaptation of periodic motion primitives based on force feedback and human coaching interaction

Dynamic movement primitives (DMP) allow efficient learning and control of complex robot behaviors for both periodic and discrete point-to-point movements either in joint or Cartesian space. They also allow efficient modulation by changing of parameters. In this paper we introduce and evaluate the means of adapting periodic DMP trajectories with respect to force feedback. We simultaneously consider two aspects: 1) adaptation of whole trajectories to comply with the constraints set by the environment; and 2) partially modifying the trajectories during the execution based on human intervention to improve the task performance. The latter can either be force-based, i. e. through physical contact, or through predefined gestures. By intervening when necessary the human acts as a tutor, instructing the robot how to modify the trajectory and bypassing the need to learn new trajectories by autonomous exploration. We introduce the approach in the context of wiping a surface, where the robot first has to acquire and maintain contact, and where later the human tutor modifies the originally learned trajectory in order to achieve the desired robot behavior. We present simulation and real world results of wiping a surface with a Kuka 7 degree-of-freedom LWR robot.