Distributed Virtual Model Control for Scalable Human-Robot Collaboration in Shared Workspace
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866915807390334976 |
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| author | Zhang, Yi Faris, Omar Sirithunge, Chapa Chu, Kai-Fung Iida, Fumiya Forni, Fulvio |
| author_facet | Zhang, Yi Faris, Omar Sirithunge, Chapa Chu, Kai-Fung Iida, Fumiya Forni, Fulvio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_17415 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Distributed Virtual Model Control for Scalable Human-Robot Collaboration in Shared Workspace Zhang, Yi Faris, Omar Sirithunge, Chapa Chu, Kai-Fung Iida, Fumiya Forni, Fulvio Robotics Systems and Control 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. |
| title | Distributed Virtual Model Control for Scalable Human-Robot Collaboration in Shared Workspace |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2602.17415 |