Learning inverse dynamics for redundant manipulator control

High performance control of robotic systems, including the new generation of humanoid, assistive and entertainment robots, requires adequate knowledge of the dynamics of the system. This can be problematic in the presence of modeling uncertainties as the performance of classical, model-based controllers is highly dependant upon accurate knowledge of the system. In addition, future robotic systems such as humanoids are likely to be redundant, requiring a mechanism for redundancy resolution when performing lower degree-of-freedom tasks. In this paper, a learning approach to estimating the inverse dynamic equations is presented. Locally Weighted Projection Regression (LWPR) is used to learn the inverse dynamics of a manipulator in both joint and task space and the resulting controllers are used to drive a 3 and 4 DOF robot in simulation. The performance of the learning controllers is compared to a traditional model based control method and is also shown to be a viable control method for a redundant system.