Learning Basis Representations of Inverse Dynamics Models for Real-Time Adaptive Control

In this paper, we propose a novel approach for adaptive control of robotic manipulators. Our approach uses a representation of inverse dynamics models learned from a varied set of training data with multiple conditions obtained from a robot. Since the representation contains various inverse dynamics models for the multiple conditions, adjusting a linear coefficient vector of the representation efficiently provides real-time adaptive control for unknown conditions rather than solving a high-dimensional learning problem. Using this approach for adaptive control of a trajectory-tracking problem with an anthropomorphic manipulator in simulations demonstrated the feasibility of the approach.