Online multi-target learning of inverse dynamics models for computed-torque control of compliant manipulators

Inverse dynamics models are applied to a plethora of robot control tasks such as computed-torque control, which are essential for trajectory execution. The analytical derivation of such dynamics models for robotic manipulators can be challenging and depends on their physical characteristics. This paper proposes a machine learning approach for modeling inverse dynamics and provides information about its implementation on a physical robotic system. The proposed algorithm can perform online multi-target learning, thus allowing efficient implementations on real robots. Our approach has been tested both offline, on datasets captured from three different robotic systems and online, on a physical system. The proposed algorithm exhibits state-of-the-art performance in terms of generalization ability and convergence. Furthermore, it has been implemented within ROS for controlling a Baxter robot. Evaluation results show that its performance is comparable to the built-in inverse dynamics model of the robot.