Task-centric Optimization of Configurations for Assistive Robots

TL;DR

This paper introduces TOC, an algorithm that optimizes robot configurations for assistive tasks, improving reachability and task success rates despite uncertainties, with applications demonstrated on a PR2 robot in simulation.

Contribution

The paper presents a novel task-centric optimization algorithm (TOC) that finds multiple effective robot configurations for assistive tasks, handling complex geometry and uncertainties.

Findings

TOC achieved a 90.6% success rate in simulation.

Compared to baseline methods, TOC significantly improved task success.

TOC can optimize configurations for multiple robots and environments.

Abstract

Robots can provide assistance to a human by moving objects to locations around the person's body. With a well chosen initial configuration, a robot can better reach locations important to an assistive task despite model error, pose uncertainty and other sources of variation. However, finding effective configurations can be challenging due to complex geometry, a large number of degrees of freedom, task complexity and other factors. We present task-centric optimization of robot configurations (TOC), which is an algorithm that finds configurations from which the robot can better reach task-relevant locations and handle task variation. Notably, TOC can return more than one configuration that when used sequentially enable a simulated assistive robot to reach more task-relevant locations. TOC performs substantial offline computation to generate a function that can be applied rapidly online to select robot configurations based on current observations. TOC explicitly models the task, environment, and user, and implicitly handles error using representations of robot dexterity. We evaluated TOC in simulation with a PR2 assisting a user with 9 assistive tasks in both a wheelchair and a robotic bed. TOC had an overall average success rate of 90.6\% compared to 50.4\%, 43.5\%, and 58.9\% for three baseline methods from literature. We additionally demonstrate how TOC can find configurations for more than one robot and can be used to assist in designing or optimizing environments.