Online Simultaneous Semi-Parametric Dynamics Model Learning

TL;DR

This paper introduces a novel consistency transform for semi-parametric dynamics models, enabling continuous online adaptation of both parametric and non-parametric components in robotic systems, improving tracking accuracy.

Contribution

The paper presents a new consistency transform that allows simultaneous online adaptation of semi-parametric models without performance degradation.

Findings

Improved tracking performance during online learning.

Effective transfer of contribution between model components.

Validated on a Kuka LWR IV manipulator.

Abstract

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.