From Raw Data to Shared 3D Semantics: Task-Oriented Communication for Multi-Robot Collaboration
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917260171411456 |
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| author | Xue, Ruibo Tan, Jiedan Liu, Fang Tong, Jingwen Wang, Taotao Wang, Shuoyao |
| author_facet | Xue, Ruibo Tan, Jiedan Liu, Fang Tong, Jingwen Wang, Taotao Wang, Shuoyao |
| contents | Multi-robot systems (MRS) rely on exchanging raw sensory data to cooperate in complex three-dimensional (3D) environments. However, this strategy often leads to severe communication congestion and high transmission latency, significantly degrading collaboration efficiency. This paper proposes a decentralized task-oriented semantic communication framework for multi-robot collaboration in unknown 3D environments. Each robot locally extracts compact, task-relevant semantics using a lightweight Pixel Difference Network (PiDiNet) with geometric processing. It shares only these semantic updates to build a task-sufficient 3D scene representation that supports cooperative perception, navigation, and object transport. Our numerical results show that the proposed method exhibits a dramatic reduction in communication overhead from $858.6$ Mb to $4.0$ Mb (over $200\times$ compression gain) while improving collaboration efficiency by shortening task completion from $1,054$ to $281$ steps. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_08624 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | From Raw Data to Shared 3D Semantics: Task-Oriented Communication for Multi-Robot Collaboration Xue, Ruibo Tan, Jiedan Liu, Fang Tong, Jingwen Wang, Taotao Wang, Shuoyao Networking and Internet Architecture Multi-robot systems (MRS) rely on exchanging raw sensory data to cooperate in complex three-dimensional (3D) environments. However, this strategy often leads to severe communication congestion and high transmission latency, significantly degrading collaboration efficiency. This paper proposes a decentralized task-oriented semantic communication framework for multi-robot collaboration in unknown 3D environments. Each robot locally extracts compact, task-relevant semantics using a lightweight Pixel Difference Network (PiDiNet) with geometric processing. It shares only these semantic updates to build a task-sufficient 3D scene representation that supports cooperative perception, navigation, and object transport. Our numerical results show that the proposed method exhibits a dramatic reduction in communication overhead from $858.6$ Mb to $4.0$ Mb (over $200\times$ compression gain) while improving collaboration efficiency by shortening task completion from $1,054$ to $281$ steps. |
| title | From Raw Data to Shared 3D Semantics: Task-Oriented Communication for Multi-Robot Collaboration |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2602.08624 |