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

• URL: http://arxiv.org/abs/2008.00699v1
• Date: Mon, 3 Aug 2020 08:04:15 GMT
• Title: Getting to Know One Another: Calibrating Intent, Capabilities and Trust for Human-Robot Collaboration
• Authors: Joshua Lee, Jeffrey Fong, Bing Cai Kok, Harold Soh
• Abstract summary: We focus on scenarios where the robot is attempting to assist a human who is unable to directly communicate her intent. We adopt a decision-theoretic approach and propose the TICC-POMDP for modeling this setting. Experiments show our approach leads to better team performance both in simulation and in a real-world study with human subjects.
• Score: 13.895990928770459
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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