Concurrent Policy Blending and System Identification for Generalized Assistive Control

TL;DR

This paper introduces a method combining policy blending with system identification to create adaptable robotic control policies that are robust to system parameter variations, demonstrated on a collaborative robot with human impairments.

Contribution

The work presents a novel approach that integrates system identification with policy blending, enabling more robust and adaptable control policies for complex tasks.

Findings

Effective handling of parameter changes in robotic tasks

Superior performance compared to domain randomization

Applicable to assistive robotic scenarios

Abstract

In this work, we address the problem of solving complex collaborative robotic tasks subject to multiple varying parameters. Our approach combines simultaneous policy blending with system identification to create generalized policies that are robust to changes in system parameters. We employ a blending network whose state space relies solely on parameter estimates from a system identification technique. As a result, this blending network learns how to handle parameter changes instead of trying to learn how to solve the task for a generalized parameter set simultaneously. We demonstrate our scheme's ability on a collaborative robot and human itching task in which the human has motor impairments. We then showcase our approach's efficiency with a variety of system identification techniques when compared to standard domain randomization.