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Many day-to-day activities involve people working collaboratively towardreaching a desired outcome. Previous research in motor control and neurosciencehave proposed inter-personal motor synergy (IPMS) as a mechanism ofcollaboration between people, referring to the idea of how two or more peoplemay work together "as if they were one" to coordinate their motion. In motorcontrol literature, uncontrolled manifold (UCM) is used for quantifying IPMS.According to this approach, coordinated motion is achieved throughstabilization of a performance variable (e.g., an output in a collaborativeoutput tracking task). We show that the UCM approach is closely related to thewell-studied consensus approach in multi-agent systems that concerns processesby which a set of interacting agents agree on a shared objective. To explorethe connection between these two approaches, in this paper, we provide acontrol-theoretic model that represents the systems-level behaviors in acollaborative task. In particular, we utilize the consensus protocol and showhow the model can be systematically tuned to reproduce the behavior exhibitedby human-human collaboration experiments. We discuss the association betweenthe proposed control law and the UCM approach and validate our model usingexperimental results previously collected from an inter-personal finger forceproduction task.

Modeling Human-Human Collaboration: A Connection Between Inter-Personal Motor Synergy and Consensus Algorithms