Customized Handling of Unintended Interface Operation in Assistive Robots

TL;DR

This paper introduces an assistance system for robot teleoperation that infers user intentions to correct unintended commands, reducing errors and improving user experience through model-based inference and validation with human subjects.

Contribution

It presents a novel intention inference approach for assistive robots that explicitly models physical interactions and provides customized corrections during teleoperation.

Findings

Significantly reduced task completion time.

Lowered cognitive workload and user frustration.

Improved overall user satisfaction.

Abstract

We present an assistance system that reasons about a human's intended actions during robot teleoperation in order to provide appropriate corrections for unintended behavior. We model the human's physical interaction with a control interface during robot teleoperation and distinguish between intended and measured physical actions explicitly. By reasoning over the unobserved intentions using model-based inference techniques, our assistive system provides customized corrections on a user's issued commands. We validate our algorithm with a 10-person human subject study in which we evaluate the performance of the proposed assistance paradigms. Our results show that the assistance paradigms helped to significantly reduce task completion time, number of mode switches, cognitive workload, and user frustration and improve overall user satisfaction.