Maurice Lamb

To Pass or Not to Pass: Modeling the Movement and Affordance Dynamics of a Pick and Place Task

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 actors choice to move an object alone or to pass it to their co-actor and (2) the trajectory dynamics of an actors 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 a behavioral dynamics model. Simulations suggest that the proposed behavioral dynamics model exhibits features observed in human participants including hysteresis in decision making, non-straight line 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.

Modeling Embedded Interpersonal and Multiagent Coordination

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 be ...

The Paths We Pick Together: A Behavioral Dynamics Algorithm for an HRI Pick-and-Place Task

Behavioral dynamics models provide an observationally grounded basis for HRI algorithms and provide another tool for creating robust, natural, and interpretable HRI systems. Here, an HRI pick-and-place algorithm was implemented based on a behavioral dynamics model of human decision-making dynamics in an interpersonal pick-and-place task. Participants were able to complete the HRI pick-and-place task, we provide comparisons to HHI pick-and-place results.

PAPAc: A Pick and Place Agent Based on Human Behavioral Dynamics

Humans often engage in tasks that require or are made more efficient by coordinating with other humans. The coordination involved in these tasks can be understood in terms of the behavioral and affordance dynamics of socially embedded agents engaged in joint action activities. Behavioral dynamics provide mathematical (differential equation) models of human behavior and interaction and affordance dynamics identify and model the ways that an agents action capabilities evolve over time. Taken together, models of human joint-action based on these approaches may provide a basis for developing robust, natural, and easy to engage artificial agents. In this paper we introduce behavioral and affordance dynamics models of human joint action in a pick-and-place task. Based on these models we provide a proof of concept pick-and-place artificial agent and implement the agent in a 3D virtual environment to interact with human co-actors.