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arXiv · 2602.08624

From Raw Data to Shared 3D Semantics: Task-Oriented Communication for Multi-Robot Collaboration

Ruibo Xue·Jiedan Tan·Fang Liu·Jingwen Tong·Taotao Wang·Shuoyao Wang
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Abstract

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.

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Ruibo Xue, Jiedan Tan, Fang Liu, Jingwen Tong, Taotao Wang, Shuoyao Wang. 2026-02-09. From Raw Data to Shared 3D Semantics: Task-Oriented Communication for Multi-Robot Collaboration. https://arxiv.org/abs/2602.08624

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