Proactive Intention Recognition for Joint Human-Robot Search and Rescue Missions through Monte-Carlo Planning in POMDP Environments

TL;DR

This paper introduces a proactive intention recognition method for search and rescue robots, using Monte-Carlo planning in POMDPs to predict responder intentions and improve exploration efficiency.

Contribution

It presents an active intention recognition framework that predicts responder intentions under sensory constraints using Monte-Carlo planning with entropy-based reward augmentation.

Findings

Significant improvements over basic approaches in simulation

Effective intention prediction under sensory constraints

Enhanced exploration strategies for search and rescue

Abstract

Proactively perceiving others' intentions is a crucial skill to effectively interact in unstructured, dynamic and novel environments. This work proposes a first step towards embedding this skill in support robots for search and rescue missions. Predicting the responders' intentions, indeed, will enable exploration approaches which will identify and prioritise areas that are more relevant for the responder and, thus, for the task, leading to the development of safer, more robust and efficient joint exploration strategies. More specifically, this paper presents an active intention recognition paradigm to perceive, even under sensory constraints, not only the target's position but also the first responder's movements, which can provide information on his/her intentions (e.g. reaching the position where he/she expects the target to be). This mechanism is implemented by employing an extension of Monte-Carlo-based planning techniques for partially observable environments, where the reward function is augmented with an entropy reduction bonus. We test in simulation several configurations of reward augmentation, both information theoretic and not, as well as belief state approximations and obtain substantial improvements over the basic approach.