Synthesizing compliant reaching movements by searching a database of example trajectories

We address the problem of generating new compliant reaching movements by searching a structured database of example trajectories. The proposed control framework is a multi-step process, where in the first step a human tutor teaches the robot how to perform a set of example reaching movements. In the second step, the recorded motion trajectories are executed with different velocities using a high gain feedback controller, for the purpose of learning corresponding torque control signals. The commanded torques are measured and stored together with the trajectory data. This data is organized in a hierarchical, graph-like structure, thereby providing the basis for search for new compliant trajectories, which can consist of parts of the previously acquired example movements. The proposed approach can construct a complete representation for newly discovered movements, including the feedforward torque commands. Finally, in the last step, the motion is executed using a low gain feedback controller and the associated feedforward torque signal. This ensures sufficient tracking accuracy and at the same time compliant behavior, which allows smooth interaction with the environment and is safe for cooperative task execution with humans. The usefulness of the proposed method was shown on a Kuka LWR robot.