Towards Intention Recognition for Robotic Assistants Through Online POMDP Planning
Juan Carlos Saborio, Joachim Hertzberg
TL;DR
A partially observable model for online intention recognition is described, some preliminary experimental results are shown and some of the challenges present in this family of problems are discussed.
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.