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

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 u...

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Published in:arXiv.org 2020-08
Main Authors: Lee, Joshua, Fong, Jeffrey, Bing Cai Kok, Soh, Harold
Format: Article
Language:English
Subjects:
Calibration
Collaboration
Computer simulation
Decision theory
Robots
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creator Lee, Joshua
Fong, Jeffrey
Bing Cai Kok
Soh, Harold
description 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.
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subjects Calibration
Collaboration
Computer simulation
Decision theory
Robots
title Getting to Know One Another: Calibrating Intent, Capabilities and Trust for Human-Robot Collaboration
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