A Bayesian-Based Approach to Human Operator Intent Recognition in Remote Mobile Robot Navigation

TL;DR

This paper introduces a Bayesian probabilistic method for recognizing human operator intent in remote robot navigation, enhancing AI assistance by accurately interpreting navigational goals during teleoperation.

Contribution

The paper presents the Bayesian Operator Intent Recognition (BOIR) framework, integrating multiple observation sources and an active intent model for improved intent detection in teleoperated robots.

Findings

BOIR outperforms existing methods in accuracy.

BOIR effectively handles various map and obstacle scenarios.

The approach provides reliable uncertainty estimates.

Abstract

This paper addresses the problem of human operator intent recognition during teleoperated robot navigation. In this context, recognition of the operator's intended navigational goal, could enable an artificial intelligence (AI) agent to assist the operator in an advanced human-robot interaction framework. We propose a Bayesian Operator Intent Recognition (BOIR) probabilistic method that utilizes: (i) an observation model that fuses information as a weighting combination of multiple observation sources providing geometric information; (ii) a transition model that indicates the evolution of the state; and (iii) an action model, the Active Intent Recognition Model (AIRM), that enables the operator to communicate their explicit intent asynchronously. The proposed method is evaluated in an experiment where operators controlling a remote mobile robot are tasked with navigation and exploration under various scenarios with different map and obstacle layouts. Results demonstrate that BOIR outperforms two related methods from literature in terms of accuracy and uncertainty of the intent recognition.