Modeling Human-Human Collaboration: A Connection Between Inter-Personal Motor Synergy and Consensus Algorithms
Sara Honarvar, Jin-OH Hahn, Tim Kiemel, Jae Kun Shim, and Yancy, Diaz-Mercado

TL;DR
This paper links inter-personal motor synergy with consensus algorithms, providing a control-theoretic model that captures human collaboration behaviors and is validated through experimental data.
Contribution
It introduces a novel connection between motor control theories and multi-agent consensus algorithms, offering a systematic model for human collaboration.
Findings
The control model reproduces human collaboration behaviors.
The UCM approach is related to consensus protocols.
Experimental validation confirms the model's effectiveness.
Abstract
Many day-to-day activities involve people working collaboratively toward reaching a desired outcome. Previous research in motor control and neuroscience have proposed inter-personal motor synergy (IPMS) as a mechanism of collaboration between people, referring to the idea of how two or more people may work together "as if they were one" to coordinate their motion. In motor control literature, uncontrolled manifold (UCM) is used for quantifying IPMS. According to this approach, coordinated motion is achieved through stabilization of a performance variable (e.g., an output in a collaborative output tracking task). We show that the UCM approach is closely related to the well-studied consensus approach in multi-agent systems that concerns processes by which a set of interacting agents agree on a shared objective. To explore the connection between these two approaches, in this paper, we…
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Taxonomy
TopicsAction Observation and Synchronization · Motor Control and Adaptation
