Planning with Trust for Human-Robot Collaboration

Trust is essential for human-robot collaboration and user adoption of autonomous systems, such as robot assistants. This paper introduces a computational model which integrates trust into robot decision-making. Specifically, we learn from data a partially observable Markov decision process (POMDP) w...

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Vydáno v:2018 13th ACM/IEEE International Conference on Human-Robot Interaction (HRI) s. 307 - 315
Hlavní autoři: Chen, Min, Nikolaidis, Stefanos, Soh, Harold, Hsu, David, Srinivasa, Siddhartha
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: New York, NY, USA ACM 26.02.2018
Edice:ACM Conferences
Témata:
Collaboration
Computational modeling
Computing methodologies > Artificial intelligence > Planning and scheduling > Planning under uncertainty
Data models
Decision making
Fluids
Human-centered computing > Human computer interaction (HCI) > Interaction paradigms > Collaborative interaction
Human-robot collaboration
Human-robot interaction
Markov decision process (POMDP)
Markov processes
Partially observable
Trust models
human-robot collaboration
trust models
partially observable markov decision process (pomdp)
ISBN:9781450349536, 1450349536
ISSN:2167-2148
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Abstract Trust is essential for human-robot collaboration and user adoption of autonomous systems, such as robot assistants. This paper introduces a computational model which integrates trust into robot decision-making. Specifically, we learn from data a partially observable Markov decision process (POMDP) with human trust as a latent variable. The trust-POMDP model provides a principled approach for the robot to (i) infer the trust of a human teammate through interaction, (ii) reason about the effect of its own actions on human behaviors, and (iii) choose actions that maximize team performance over the long term. We validated the model through human subject experiments on a table-clearing task in simulation (201 participants) and with a real robot (20 participants). The results show that the trust-POMDP improves human-robot team performance in this task. They further suggest that maximizing trust in itself may not improve team performance.
AbstractList Trust is essential for human-robot collaboration and user adoption of autonomous systems, such as robot assistants. This paper introduces a computational model which integrates trust into robot decision-making. Specifically, we learn from data a partially observable Markov decision process (POMDP) with human trust as a latent variable. The trust-POMDP model provides a principled approach for the robot to (i) infer the trust of a human teammate through interaction, (ii) reason about the effect of its own actions on human behaviors, and (iii) choose actions that maximize team performance over the long term. We validated the model through human subject experiments on a table-clearing task in simulation (201 participants) and with a real robot (20 participants). The results show that the trust-POMDP improves human-robot team performance in this task. They further suggest that maximizing trust in itself may not improve team performance.
Author Soh, Harold
Chen, Min
Srinivasa, Siddhartha
Nikolaidis, Stefanos
Hsu, David
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Keywords human-robot collaboration
trust models
partially observable markov decision process (pomdp)
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Snippet Trust is essential for human-robot collaboration and user adoption of autonomous systems, such as robot assistants. This paper introduces a computational model...
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SubjectTerms Collaboration
Computational modeling
Computing methodologies -- Artificial intelligence -- Planning and scheduling -- Planning under uncertainty
Data models
Decision making
Fluids
Human-centered computing -- Human computer interaction (HCI) -- Interaction paradigms -- Collaborative interaction
Human-robot collaboration
Human-robot interaction
Markov decision process (POMDP)
Markov processes
Partially observable
Trust models
Title Planning with Trust for Human-Robot Collaboration
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