title: DOOMED: Direct Online Optimization of Modeling Errors in Dynamics.
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.
authors.affiliation: 1 Lula Robotics, Inc. , Seattle, Washington.
authors.forename: Nathan
authors.initials: N
authors.lastname: Ratliff
authors.affiliation: 1 Lula Robotics, Inc. , Seattle, Washington.|2 Autonomous Motion Department, MPI for Intelligent Systems , Tübingen, Germany .|3 CLMC Lab, University of Southern California , Los Angeles, California.
authors.forename: Franziska
authors.initials: F
authors.lastname: Meier
authors.affiliation: 1 Lula Robotics, Inc. , Seattle, Washington.|2 Autonomous Motion Department, MPI for Intelligent Systems , Tübingen, Germany .
authors.forename: Daniel
authors.initials: D
authors.lastname: Kappler
authors.affiliation: 2 Autonomous Motion Department, MPI for Intelligent Systems , Tübingen, Germany .|3 CLMC Lab, University of Southern California , Los Angeles, California.
authors.forename: Stefan
authors.initials: S
authors.lastname: Schaal
keywords: adaptive control; feedback control; inverse dynamics; learning control; manipulation; online learning