Towards Intention Recognition for Robotic Assistants Through Online POMDP Planning

TL;DR

This paper presents an online intention recognition model for robotic assistants using POMDP planning, addressing challenges in noisy, incomplete data and active information gathering in industrial environments.

Contribution

It introduces a partially observable model for intention recognition that integrates online POMDP planning to improve robotic assistance in complex settings.

Findings

Preliminary experimental results demonstrate feasibility.

Highlights challenges in noisy observation environments.

Proposes active goal recognition approach.

Abstract

Intention recognition, or the ability to anticipate the actions of another agent, plays a vital role in the design and development of automated assistants that can support humans in their daily tasks. In particular, industrial settings pose interesting challenges that include potential distractions for a decision-maker as well as noisy or incomplete observations. In such a setting, a robotic assistant tasked with helping and supporting a human worker must interleave information gathering actions with proactive tasks of its own, an approach that has been referred to as active goal recognition. In this paper we describe a partially observable model for online intention recognition, show some preliminary experimental results and discuss some of the challenges present in this family of problems.