Research

Paper

TESTING February 19, 2026

Distributed Virtual Model Control for Scalable Human-Robot Collaboration in Shared Workspace

Authors

Yi Zhang, Omar Faris, Chapa Sirithunge, Kai-Fung Chu, Fumiya Iida, Fulvio Forni

Abstract

We present a decentralized, agent agnostic, and safety-aware control framework for human-robot collaboration based on Virtual Model Control (VMC). In our approach, both humans and robots are embedded in the same virtual-component-shaped workspace, where motion is the result of the interaction with virtual springs and dampers rather than explicit trajectory planning. A decentralized, force-based stall detector identifies deadlocks, which are resolved through negotiation. This reduces the probability of robots getting stuck in the block placement task from up to 61.2% to zero in our experiments. The framework scales without structural changes thanks to the distributed implementation: in experiments we demonstrate safe collaboration with up to two robots and two humans, and in simulation up to four robots, maintaining inter-agent separation at around 20 cm. Results show that the method shapes robot behavior intuitively by adjusting control parameters and achieves deadlock-free operation across team sizes in all tested scenarios.

Metadata

arXiv ID: 2602.17415
Provider: ARXIV
Primary Category: cs.RO
Published: 2026-02-19
Fetched: 2026-02-21 18:51

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