Human social motor solutions for human–machine interaction in dynamical task contexts

Significance Human–machine interaction (HMI) is becoming ubiquitous within today’s society due to rapid advances in interactive virtual and robotic technologies. Ensuring the real-time coordination necessary for effective HMI, however, requires both identifying the dynamics of natural human multiagent performance and formally modeling those dynamics in ways that can be incorporated into the control structure of artificial agents. Here, we used a dyadic shepherding task to demonstrate that the dynamics of complex human multiagent activity cannot only be effectively modeled by means of simple, environmentally coupled dynamical motor primitives but that such models can be easily incorporated into the control structure of artificial agents to achieve robust, human-level HMI performance. 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.