Online learning of task-specific dynamics for periodic tasks

In this paper we address the problem of accurate trajectory tracking while ensuring compliant robotic behaviour for periodic tasks. We propose an approach for on-line learning of task-specific dynamics, i.e. task specific movement trajectories and corresponding force/torque profiles. The proposed control framework is a multi-step process, where in the first step a human tutor shows how to perform the desired periodic task. A state estimator based on an adaptive frequency oscillator combined with dynamic movement primitives is employed to extract movement trajectories. In the second step, the movement trajectory is accurately executed in the controlled environment under human supervision. In this step, the robot is accurately tracking the acquired movement trajectory, using high feedback gains to ensure accurate tracking. Thus it can learn the corresponding force/torque profiles, i. e. task-specific dynamics. Finally, in the third step, the movement is executed with the learned feedforward task-specific dynamic model, allowing for low position feedback gains, which implies compliant robot behaviour. Thus, it is safe for interaction with humans or the environment. The proposed approach was evaluated on a Kuka LRW robot performing object manipulation and crank turning.