Human-Robot Teaming: Approaches from Joint Action and Dynamical Systems

Iqbal, Tarıq; Riek, Laurel D.

doi:10.1007/978-94-007-6046-2_137

Cited by 26 publications

(14 citation statements)

References 58 publications

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“…In human teams, communication is one of the most important team processes since it facilitates formation of shared SA, shared mental models, and goal alignment (as noted above). The extent to which communication mechanisms support a wide range of emotional and social cues enhances team effectiveness (Hanumantharao and Grabowski, 2006) and can help facilitate the view of robots as teammates (Iqbal and Riek, 2017). HATs need to be able to establish common ground, communicate shared mental models, shared goals, and shared understanding of the world (Schaefer et al, 2017).…”

Section: Communicationmentioning

confidence: 99%

“…Having these capabilities will allow the HAT to begin to anticipate the needs, actions, and future problems within the team. Anticipation of needs and future actions is a critical teaming function (Liu et al, 2016;Demir et al, 2017;Iqbal and Riek, 2017;Chen et al, 2018) since it allows mutual adaptation and a greater degree of team coordination and synchronization (this has also been labeled fluency; Hoffman and Breazeal, 2007). Shared mental models is more than merely all team members sharing every bit of information with one another; such a strategy would be highly inefficient both in terms of computation and communication bandwidth.…”

Section: Shared Mental Modelsmentioning

confidence: 99%

“…Considerable work has been done on human-robot interaction in proximate space. Iqbal and Riek (2017) discuss methods to promote awareness of the action of others (e.g., a human working with a robot) in proximate space. Specifically, they have developed methods to allow the robot to anticipate and react to complementary actions from a human partner and these methods have been shown to improve human-robot coordination and performance in tasks such as hand-overs and joint work in small, shared spaces.…”

Section: Influence On the Team As A Wholementioning

confidence: 99%

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Human–Autonomy Teaming: Definitions, Debates, and Directions

et al. 2021

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Researchers are beginning to transition from studying human–automation interaction to human–autonomy teaming. This distinction has been highlighted in recent literature, and theoretical reasons why the psychological experience of humans interacting with autonomy may vary and affect subsequent collaboration outcomes are beginning to emerge (de Visser et al., 2018; Wynne and Lyons, 2018). In this review, we do a deep dive into human–autonomy teams (HATs) by explaining the differences between automation and autonomy and by reviewing the domain of human–human teaming to make inferences for HATs. We examine the domain of human–human teaming to extrapolate a few core factors that could have relevance for HATs. Notably, these factors involve critical social elements within teams that are central (as argued in this review) for HATs. We conclude by highlighting some research gaps that researchers should strive toward answering, which will ultimately facilitate a more nuanced and complete understanding of HATs in a variety of real-world contexts.

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Section: Communicationmentioning

confidence: 99%

Section: Shared Mental Modelsmentioning

confidence: 99%

Section: Influence On the Team As A Wholementioning

confidence: 99%

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Human–Autonomy Teaming: Definitions, Debates, and Directions

et al. 2021

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“…Integrated supply chain planning with execution feedback schedules semi-autonomous fulfillment-oriented human-robot collaboration (HRC) (Bauer et al, 2008) and intelligent machine service (iMS)-teams (Hoffman and Breazeal, 2004;Herneth, 2018;Iqbal and Riek, 2019). Resulting adaptive supply networks (ASN) are digital and secure networks for autonomous collaboration and intelligent decision-making (Rodewald et al, 2015;Herneth, 2019).…”

Section: Intra-organizational Maturitymentioning

confidence: 99%

Smart Contracts for Sustainable Supply Chain Management: Conceptual Frameworks for Supply Chain Maturity Evaluation and Smart Contract Sustainability Assessment

2021

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Current research on smart contracts focuses on technical, conceptual, and legal aspects but neglects organizational requirements and sustainability impacts. We consider this a significant research gap and explore the relationship between smart contracts and sustainability in supply chains. First, we define the concept of smart contracts in terms of supply chain management. Then, we conduct a content analysis of the literature to explore the overlapping research fields of smart contracts and sustainability in supply chains. Next, we develop a semi-structured assessment framework to model the potential environmental and social impacts induced by smart contracts on supply chains. We propose a conceptual framework for supply chain maturity by mapping the relationships between organizational development, sustainability, and technology. We identify smart contracts as a foundational technology that enables efficient and transparent governance and collaborative self-coordination of human and non-human actors. Thus, we argue that smart contracts can contribute to the economic and social development of networked value chains and Society 5.0. To stimulate interdisciplinary research on smart contracts, we conclude the article by formulating research propositions and trade-offs for smart contracts in the context of technology development, business process and supply chain management, and sustainability.

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“…In the following section, we will review relevant work on probabilistic modeling of joint actions and multimodal modeling. For a detailed discussion of computational techniques in the HRI domain, see the excellent surveys in [13,27]. Probabilistic Modeling of Joint Actions: Early work on modeling HRI scenarios using probabilistic representations focused on HMMs [23] as a method of choice, see for instance the works in [15,26].…”

Section: Related Workmentioning

confidence: 99%

Probabilistic Multimodal Modeling for Human-Robot Interaction Tasks

Campbell¹,

Stepputtis²,

Amor³

2019

Robotics: Science and Systems XV

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Human-robot interaction benefits greatly from multimodal sensor inputs as they enable increased robustness and generalization accuracy. Despite this observation, few HRI methods are capable of efficiently performing inference for multimodal systems. In this work, we introduce a reformulation of Interaction Primitives which allows for learning from demonstration of interaction tasks, while also gracefully handling nonlinearities inherent to multimodal inference in such scenarios. We also empirically show that our method results in more accurate, more robust, and faster inference than standard Interaction Primitives and other common methods in challenging HRI scenarios.

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Human-Robot Teaming: Approaches from Joint Action and Dynamical Systems

Cited by 26 publications

References 58 publications

Human–Autonomy Teaming: Definitions, Debates, and Directions

Human–Autonomy Teaming: Definitions, Debates, and Directions

Smart Contracts for Sustainable Supply Chain Management: Conceptual Frameworks for Supply Chain Maturity Evaluation and Smart Contract Sustainability Assessment

Probabilistic Multimodal Modeling for Human-Robot Interaction Tasks

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