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1. Who's Leading This Dance?: Theorizing Automatic and Strategic Synchrony in Human-Exoskeleton Interactions

   https://doi.org/10.3389/fpsyg.2021.624108  

   Kirkwood, Gavin Lawrence; Otmar, Christopher D.; Hansia, Mohemmad (February 2021, Frontiers in Psychology)

   null (Ed.)

   Wearable robots are an emerging form of technology that allow organizations to combine the strength, precision, and performance of machines with the flexibility, intelligence, and problem-solving abilities of human wearers. Active exoskeletons are a type of wearable robot that gives wearers the ability to effortlessly lift up to 200 lbs., as well as perform other types of physically demanding tasks that would be too strenuous for most humans. Synchronization between exoskeleton suits and wearers is one of the most challenging requirements to operate these technologies effectively. In this conceptual paper, we extend interpersonal adaption theory (IAT) to the exoskeleton context and explicate (a) the antecedents that are most likely to shape synchrony in human-exoskeleton interactions, (b) automatic and strategic synchrony as adaptive behaviors in human-exoskeleton interactions, and (c) outcome variables that are especially important in these processes. Lastly, we offer a discussion of key methodological challenges for measuring synchrony in human-exoskeleton interactions and offer a future research agenda for this important area. 

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2. Designing An Augmented Reality Based Interface For Wearable Exoskeletons

   https://doi.org/10.1177/1071181320641012  

   Kulkarni, Chaitanya; Hsing, Hsiang-Wen; Kandi, Dina; Kommaraju, Shriya; Lau, Nathan; Srinivasan, Divya (December 2020, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   Full-body, powered wearable exoskeletons combine the capabilities of machines and humans to maximize productivity. Powered exoskeletons can ease industrial workers in manipulating heavy loads in a manner that is difficult to automate. However, introduction of exoskeletons may result in unexpected work hazards, due to the mismatch between user-intended and executed actions thereby creating difficulties in sensing the physical operational envelope, need for increased clearance, and maneuverability limitations. To control such hazards, this paper presents a rearview human localization augmented reality (AR) platform to enhance spatial awareness of people behind the exoskeleton users. This platform leverages a computer vision algorithm called Monocular 3D Pedestrian Localization and Uncertainty Estimation (MonoLoco) for identifying humans and estimating their distance from a video camera feed and off-the-shelf AR goggles for visualizing the surrounding. Augmenting rear view awareness of humans can help exoskeleton users to avoid accidental collisions that can lead to severe injuries. 

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3. Socially intelligent machines that learn from humans and help humans learn

   https://doi.org/10.1098/rsta.2022.0048  

   Gweon, Hyowon; Fan, Judith; Kim, Been (July 2023, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences)

   A hallmark of human intelligence is the ability to understand and influence other minds. Humans engage in inferential social learning (ISL) by using commonsense psychology to learn from others and help others learn. Recent advances in artificial intelligence (AI) are raising new questions about the feasibility of human–machine interactions that support such powerful modes of social learning. Here, we envision what it means to develop socially intelligent machines that can learn, teach, and communicate in ways that are characteristic of ISL. Rather than machines that simply predict human behaviours or recapitulate superficial aspects of human sociality (e.g. smiling, imitating), we should aim to build machines that can learn from human inputs and generate outputs for humans by proactively considering human values, intentions and beliefs. While such machines can inspire next-generation AI systems that learn more effectively from humans (as learners) and even help humans acquire new knowledge (as teachers), achieving these goals will also require scientific studies of its counterpart: how humans reason about machine minds and behaviours. We close by discussing the need for closer collaborations between the AI/ML and cognitive science communities to advance a science of both natural and artificial intelligence. This article is part of a discussion meeting issue ‘Cognitive artificial intelligence’. 

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4. Experiments with sensorimotor games in dynamic human/machine interaction

   https://doi.org/10.1117/12.2519258  

   Chasnov, Benjamin; Yamagami, Momona; Parsa, Behnoosh; Ratliff, Lillian J.; Burden, Samuel A. (May 2019, Micro- and Nanotechnology Sensors, Systems, and Applications XI)

   While interacting with a machine, humans will naturally formulate beliefs about the machine's behavior, and these beliefs will affect the interaction. Since humans and machines have imperfect information about each other and their environment, a natural model for their interaction is a game. Such games have been investigated from the perspective of economic game theory, and some results on discrete decision-making have been translated to the neuromechanical setting, but there is little work on continuous sensorimotor games that arise when humans interact in a dynamic closed loop with machines. We study these games both theoretically and experimentally, deriving predictive models for steady-state (i.e. equilibrium) and transient (i.e. learning) behaviors of humans interacting with other agents (humans and machines). Specifically, we consider experiments wherein agents are instructed to control a linear system so as to minimize a given quadratic cost functional, i.e. the agents play a Linear-Quadratic game. Using our recent results on gradient-based learning in continuous games, we derive predictions regarding steady-state and transient play. These predictions are compared with empirical observations of human sensorimotor learning using a teleoperation testbed. 

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5. Just Hierarchy and the Ethics of Artificial Intelligence: Two Approaches to a Relational Ethic for Artificial Intelligence

   Zhu, Qin (July 2023, Ethical Perspectives)

   Dominant approaches to the ethics of artificial intelligence (AI) systems have been mainly based on individualistic, rule-based ethical frameworks central to Western cultures. These approaches have encountered both philosophical and computational limitations. They often struggle to accommodate remarkably diverse, unstable, complex contexts of human-AI interactions. Recently there has been an increasing interest among philosophers and computer scientists in building a relational approach to the ethics of AI. This article engages with Daniel A. Bell and Pei Wang’s most recent book Just Hierarchy and explores how their theory of just hierarchy can be employed to develop a more systematic account for relational AI ethics. Bell and Wang’s theory of just hierarchy acknowledges that there are morally justified situations in which social relations are not equal. Just hierarchy can exist both between humans and between humans and machines such as AI systems. Therefore, a relational ethic for AI based on just hierarchy can include two theses: (i) AI systems should be considered merely as tools and their relations with humans are hierarchical (e.g. designing AI systems with lower moral standing than humans); and (ii) the moral assessment of AI systems should focus on whether they help us realize our rolebased moral obligations prescribed by our social relations with others (these relations often involve diverse forms of morally justified hierarchies in communities). Finally, this article will discuss the practical implications of such a relational ethic framework for designing socially integrated and ethically responsive AI systems. 

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