Getting to Know One Another: Calibrating Intent, Capabilities and Trust for Human-Robot Collaboration

TL;DR

This paper introduces a decision-theoretic framework called TICC-POMDP for calibrating intent and capabilities in human-robot collaboration, improving team performance when communication is limited.

Contribution

It presents a novel POMDP-based model and solver for calibrating human and robot capabilities and intent in collaborative tasks with limited communication.

Findings

Enhanced team performance in simulation and real-world experiments.

Effective calibration of intent and capabilities improves collaboration.

Proposed method outperforms baseline approaches.

Abstract

Common experience suggests that agents who know each other well are better able to work together. In this work, we address the problem of calibrating intention and capabilities in human-robot collaboration. In particular, we focus on scenarios where the robot is attempting to assist a human who is unable to directly communicate her intent. Moreover, both agents may have differing capabilities that are unknown to one another. We adopt a decision-theoretic approach and propose the TICC-POMDP for modeling this setting, with an associated online solver. Experiments show our approach leads to better team performance both in simulation and in a real-world study with human subjects.