IPAB Workshop-18/06/2020

 

Joshua Smith
 
Title: Online Simultaneous Semi-Parametric Dynamics Model Learning.
 

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 generalizability. In this letter, 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.

 

Jun 18 2020 -

IPAB Workshop-18/06/2020

David Sinclair, Joshua Smith
https://eu.bbcollab.com/guest/38838074cb73486a955850edf755b3a7

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