Big Data. 2016 Dec;4(4):253-268. doi: 10.1089/big.2016.0041.

DOOMED: Direct Online Optimization of Modeling Errors in Dynamics.

Big data

Nathan Ratliff, Franziska Meier, Daniel Kappler, Stefan Schaal

PMID: 27992267 DOI: 10.1089/big.2016.0041

Abstract

It has long been hoped that model-based control will improve tracking performance while maintaining or increasing compliance. This hope hinges on having or being able to estimate an accurate inverse dynamics model. As a result, substantial effort has gone into modeling and estimating dynamics (error) models. Most recent research has focused on learning the true inverse dynamics using data points mapping observed accelerations to the torques used to generate them. Unfortunately, if the initial tracking error is bad, such learning processes may train substantially off-distribution to predict well on actual observed acceleration rather than the desired accelerations. This work takes a different approach. We define a class of gradient-based online learning algorithms we term Direct Online Optimization of Modeling Errors in Dynamics (DOOMED) that directly minimize an objective measuring the divergence between actual and desired accelerations. Our objective is defined in terms of the true system's unknown dynamics and is therefore impossible to evaluate. However, we show that its gradient is observable online from system data. We develop a novel adaptive control approach based on running online learning to directly correct (inverse) dynamics errors in real time using the data stream from the robot to accurately achieve desired accelerations during execution.

Keywords: adaptive control; feedback control; inverse dynamics; learning control; manipulation; online learning

Publication Types

• Journal Article