Online Simultaneous Semi-Parametric Dynamics Model Learning
This addresses the challenge of continuous online adaptation in robot dynamics modeling, which is incremental but important for real-time control and stability.
The paper tackled the non-stationary problem in online semi-parametric dynamics model learning for robots by introducing a consistency transform, enabling simultaneous adaptation of parametric and non-parametric components without batch updates. They validated it on a Kuka LWR IV manipulator, showing improved tracking performance and a bias towards the parametric component.
Accurate models of robots' dynamics are critical for control, stability, motion optimization, and interaction. Semi-Parametric approaches to dynamics learning combine physics-based Parametric models with unstructured Non-Parametric regression with the hope to achieve both accuracy and generalizablity. In this paper we highlight the non-stationary problem created when attempting to adapt both Parametric and Non-Parametric components simultaneously. We present a consistency transform designed to compensate for this non-stationary effect, such that the contributions of both models can adapt simultaneously without adversely affecting the performance of the platform. Thus we are able to apply the Semi-Parametric learning approach for continuous iterative online adaptation, without relying on batch or offline updates. We validate the transform via a perfect virtual model as well as by applying the overall system on a Kuka LWR IV manipulator. We demonstrate improved tracking performance during online learning and show a clear transference of contribution between the two components with a learning bias towards the Parametric component.