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

TL;DR

This paper introduces a decentralized semantic communication framework for multi-robot systems that significantly reduces data transmission while enhancing collaboration efficiency in complex 3D environments.

Contribution

It presents a novel task-oriented semantic communication approach using lightweight local semantics, enabling efficient multi-robot collaboration with reduced communication load.

Findings

Over 200x reduction in communication data from 858.6 Mb to 4.0 Mb.

Improved collaboration efficiency with task completion steps reduced from 1054 to 281.

Effective scene understanding with compact semantic updates.

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.