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• W2342829595 abstract "Mutual adaptation is critical for effective team collaboration. This paper presents a formalism for human-robot mutual adaptation in collaborative tasks. We propose the bounded-memory adaptation model (BAM), which captures human adaptive behaviours based on a bounded memory assumption. We integrate BAM into a partially observable stochastic model, which enables robot adaptation to the human. When the human is adaptive, the robot will guide the human towards a new, optimal collaborative strategy unknown to the human in advance. When the human is not willing to change their strategy, the robot adapts to the human in order to retain human trust. Human subject experiments indicate that the proposed formalism can significantly improve the effectiveness of human-robot teams, while human subject ratings on the robot performance and trust are comparable to those achieved by cross training, a state-of-the-art human-robot team training practice." @default.
• W2342829595 created "2016-06-24" @default.
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• W2342829595 date "2016-03-07" @default.
• W2342829595 modified "2023-09-24" @default.
• W2342829595 title "Formalizing Human-Robot Mutual Adaptation: A Bounded Memory Model" @default.
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• W2342829595 doi "https://doi.org/10.5555/2906831.2906845" @default.
• W2342829595 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6329591" @default.
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