Formation conditions of mutual adaptation in human-agent collaborative interaction

Xu, Ye; Ohmoto, Yoshimasa; Okada, Shogo; Ueda, Kazuhiro; Komatsu, Takanori; Okadome, Takeshi; Kamei, Koji; Sumi, Y.; Nishida, Toyoaki

doi:10.1007/s10489-010-0255-y

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…A better understanding of these processes will help to initiate and maintain co-adaptation in human-agent teams. (Xu et al, 2012) have outlined requirements for co-adaptation to occur in human-robot teams. First, they argue that in order to achieve a common purpose, both agents need to be prepared to adapt their behavior to their partner, should actively and dynamically estimate the partner's intention, and develop options of how to adapt their own behavior in response.…”

Section: Co-adaptationmentioning

confidence: 99%

Identifying Interaction Patterns of Tangible Co-Adaptations in Human-Robot Team Behaviors

et al. 2021

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As robots become more ubiquitous, they will increasingly need to behave as our team partners and smoothly adapt to the (adaptive) human team behaviors to establish successful patterns of collaboration over time. A substantial amount of adaptations present themselves through subtle and unconscious interactions, which are difficult to observe. Our research aims to bring about awareness of co-adaptation that enables team learning. This paper presents an experimental paradigm that uses a physical human-robot collaborative task environment to explore emergent human-robot co-adaptions and derive the interaction patterns (i.e., the targeted awareness of co-adaptation). The paradigm provides a tangible human-robot interaction (i.e., a leash) that facilitates the expression of unconscious adaptations, such as “leading” (e.g., pulling the leash) and “following” (e.g., letting go of the leash) in a search-and-navigation task. The task was executed by 18 participants, after which we systematically annotated videos of their behavior. We discovered that their interactions could be described by four types of adaptive interactions: stable situations, sudden adaptations, gradual adaptations and active negotiations. From these types of interactions we have created a language of interaction patterns that can be used to describe tacit co-adaptation in human-robot collaborative contexts. This language can be used to enable communication between collaborating humans and robots in future studies, to let them share what they learned and support them in becoming aware of their implicit adaptations.

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Section: Co-adaptationmentioning

confidence: 99%

Identifying Interaction Patterns of Tangible Co-Adaptations in Human-Robot Team Behaviors

et al. 2021

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“…If we look at human-robot interaction literature, several terms are used that describe a similar process in which two parties or systems change their behavior and/or mental states concurrently while interacting with each other. Co-adaptation (Xu et al, 2012;Chauncey et al, 2016;Nikolaidis et al, 2017a) and co-learning (Bosch et al, 2019) are two of them, but we also encounter coevolution (Döppner et al, 2019), in which "co" stands for collaborative, also meaning "mutual. "…”

Section: Co-learning: Background and Definitionmentioning

confidence: 99%

“…There are several vision papers explaining the importance of both co-adaptation [e.g. ( Xu et al, 2012 ; Ansari, Erol, and Sihn 2018 )] as well as co-learning [e.g. ( Bosch et al, 2019 ; Holstein, Aleven, and Rummel 2020 ; Wenskovitch and North 2020 )], but a clear distinction between the two, or a definition specifically for co-learning, is missing from these papers.…”

Section: Co-learning: Background and Definitionmentioning

confidence: 99%

Becoming Team Members: Identifying Interaction Patterns of Mutual Adaptation for Human-Robot Co-Learning

2021

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Becoming a well-functioning team requires continuous collaborative learning by all team members. This is called co-learning, conceptualized in this paper as comprising two alternating iterative stages: partners adapting their behavior to the task and to each other (co-adaptation), and partners sustaining successful behavior through communication. This paper focuses on the first stage in human-robot teams, aiming at a method for the identification of recurring behaviors that indicate co-learning. Studying this requires a task context that allows for behavioral adaptation to emerge from the interactions between human and robot. We address the requirements for conducting research into co-adaptation by a human-robot team, and designed a simplified computer simulation of an urban search and rescue task accordingly. A human participant and a virtual robot were instructed to discover how to collaboratively free victims from the rubbles of an earthquake. The virtual robot was designed to be able to real-time learn which actions best contributed to good team performance. The interactions between human participants and robots were recorded. The observations revealed patterns of interaction used by human and robot in order to adapt their behavior to the task and to one another. Results therefore show that our task environment enables us to study co-learning, and suggest that more participant adaptation improved robot learning and thus team level learning. The identified interaction patterns can emerge in similar task contexts, forming a first description and analysis method for co-learning. Moreover, the identification of interaction patterns support awareness among team members, providing the foundation for human-robot communication about the co-adaptation (i.e., the second stage of co-learning). Future research will focus on these human-robot communication processes for co-learning.

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Human-AI Teaming: Following the IMOI Framework

Kleanthous

2024

Lecture Notes in Computer Science

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Formation conditions of mutual adaptation in human-agent collaborative interaction

Cited by 6 publications

References 14 publications

Identifying Interaction Patterns of Tangible Co-Adaptations in Human-Robot Team Behaviors

Identifying Interaction Patterns of Tangible Co-Adaptations in Human-Robot Team Behaviors

Becoming Team Members: Identifying Interaction Patterns of Mutual Adaptation for Human-Robot Co-Learning

Human-AI Teaming: Following the IMOI Framework

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