| 1. |
- Babajanyan, Diana, et al.
(author)
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I Know Your Next Move : Action Decisions in Dyadic Pick and Place Tasks
- 2022
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In: Proceedings of the 44th Annual Conference of the Cognitive Science Society. - : Cognitive Science Society, Inc.. ; , s. 563-570
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Conference paper (peer-reviewed)abstract
- Joint pick and place tasks occur in many interpersonal scenarios, such as when two people pick up and pass dishes. Previous studies have demonstrated that low-dimensional models can accurately capture the dynamics of pick and place motor behaviors in a controlled 2D environment. The current study models the dynamics of pick-up and pass decisions within a less restrictive virtual reality mediated 3D joint pick and place task. Findings indicate that reach-normalized distance measures, between participants and objects/targets, could accurately predict pick-up and pass decisions. Findings also reveal that participants took longer to pick-up objects where division of labor boundaries were less obvious and tended to pass in locations maximizing the dyad's efficiency. This study supports the notion that individuals are more likely to engage in interpersonal behavior when a task goal is perceived as difficult or unattainable (i.e., not afforded). Implications of findings for human-artificial agent interactions are discussed.
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| 2. |
- Lamb, Maurice, et al.
(author)
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A Hierarchical Behavioral Dynamic Approach for Naturally Adaptive Human-Agent Pick-and-Place Interactions
- 2019
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In: Complexity. - : John Wiley & Sons. - 1076-2787 .- 1099-0526.
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Journal article (peer-reviewed)abstract
- Interactive or collaborative pick-and-place tasks occur during all kinds of daily activities, for example, when two or more individuals pass plates, glasses, and utensils back and forth between each other when setting a dinner table or loading a dishwasher together. In the near future, participation in these collaborative pick-and-place tasks could also include robotic assistants. However, for human-machine and human-robot interactions, interactive pick-and-place tasks present a unique set of challenges. A key challenge is that high-level task-representational algorithms and preplanned action or motor programs quickly become intractable, even for simple interaction scenarios. Here we address this challenge by introducing a bioinspired behavioral dynamic model of free-flowing cooperative pick-and-place behaviors based on low-dimensional dynamical movement primitives and nonlinear action selection functions. Further, we demonstrate that this model can be successfully implemented as an artificial agent control architecture to produce effective and robust human-like behavior during human-agent interactions. Participants were unable to explicitly detect whether they were working with an artificial (model controlled) agent or another human-coactor, further illustrating the potential effectiveness of the proposed modeling approach for developing systems of robust real/embodied human-robot interaction more generally.
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| 3. |
- Lamb, Maurice, et al.
(author)
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To Pass or Not to Pass : Modeling the Movement and Affordance Dynamics of a Pick and Place Task
- 2017
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In: Frontiers in Psychology. - : Frontiers Media S.A.. - 1664-1078. ; 8
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Journal article (peer-reviewed)abstract
- Humans commonly engage in tasks that require or are made more efficient by coordinating with other humans. In this paper we introduce a task dynamics approach for modeling multi-agent interaction and decision making in a pick and place task where an agent must move an object from one location to another and decide whether to act alone or with a partner. Our aims were to identify and model (1) the affordance related dynamics that define an actor’s choice to move an object alone or to pass it to their co-actor and (2) the trajectory dynamics of an actor’s hand movements when moving to grasp, relocate, or pass the object. Using a virtual reality pick and place task, we demonstrate that both the decision to pass or not pass an object and the movement trajectories of the participants can be characterized in terms of behavioral dynamics model. Simulations suggest that the proposed behavioral dynamics model exhibits features observed in human participants including hysteresis in decision making, non-straight trajectories, and non-constant velocity profiles. The proposed model highlights how the same low-dimensional behavioral dynamics can operate to constrain multiple (and often nested) levels of human activity and suggests that knowledge of what, when, where and how to move or act during pick and place behavior may be defined by these low dimensional task dynamics and, thus, can emerge spontaneously and in real-time with little a priori planning.
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| 4. |
- Nalepka, Patrick, et al.
(author)
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Human social motor solutions for human-machine interaction in dynamical task contexts
- 2019
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In: Proceedings of the National Academy of Sciences of the United States of America. - : National Academy of Sciences. - 0027-8424 .- 1091-6490. ; 116:4, s. 1437-1446
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Journal article (peer-reviewed)abstract
- Multiagent activity is commonplace in everyday life and can improve the behavioral efficiency of task performance and learning. Thus, augmenting social contexts with the use of interactive virtual and robotic agents is of great interest across health, sport, and industry domains. However, the effectiveness of human–machine interaction (HMI) to effectively train humans for future social encounters depends on the ability of artificial agents to respond to human coactors in a natural, human-like manner. One way to achieve effective HMI is by developing dynamical models utilizing dynamical motor primitives (DMPs) of human multiagent coordination that not only capture the behavioral dynamics of successful human performance but also, provide a tractable control architecture for computerized agents. Previous research has demonstrated how DMPs can successfully capture human-like dynamics of simple nonsocial, single-actor movements. However, it is unclear whether DMPs can be used to model more complex multiagent task scenarios. This study tested this human-centered approach to HMI using a complex dyadic shepherding task, in which pairs of coacting agents had to work together to corral and contain small herds of virtual sheep. Human–human and human–artificial agent dyads were tested across two different task contexts. The results revealed (i) that the performance of human–human dyads was equivalent to those composed of a human and the artificial agent and (ii) that, using a “Turing-like†methodology, most participants in the HMI condition were unaware that they were working alongside an artificial agent, further validating the isomorphism of human and artificial agent behavior.
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| 5. |
- Richardson, Michael J., et al.
(author)
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Modeling embedded interpersonal and multiagent coordination
- 2016
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In: COMPLEXIS 2016 - Proceedings of the 1st International Conference on Complex Information Systems. - Setubal : SciTePress. - 9789897581816 ; , s. 155-164
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Conference paper (peer-reviewed)abstract
- Interpersonal or multiagent coordination is a common part of everyday human activity. Identifying the dynamic processes that shape and constrain the complex, time-evolving patterns of multiagent behavioral coordination often requires the development of dynamical models to test hypotheses and motivate future research questions. Here we review a task dynamic framework for modeling multiagent behavior and illustrate the application of this framework using two examples. With an emphasis on synergistic self-organization, we demonstrate how the behavioral coordination that characterizes many social activities emerges naturally from the physical, informational, and biomechanical constraints and couplings that exist between two or more environmentally embedded and mutually responsive individuals.
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