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× compression gain) while improving collaboration efficiency by shortening task completion from 1,054 to 281 steps.
@article{arxiv.2602.08624,
title = {From Raw Data to Shared 3D Semantics: Task-Oriented Communication for Multi-Robot Collaboration},
author = {Ruibo Xue and Jiedan Tan and Fang Liu and Jingwen Tong and Taotao Wang and Shuoyao Wang},
journal= {arXiv preprint arXiv:2602.08624},
year = {2026}
}