AgentComm: Semantic Communication for Embodied Agents

TL;DR

This paper introduces AgentComm, a semantic communication framework for embodied AI agents that significantly reduces bandwidth usage while maintaining task performance through semantic processing and importance-aware transmission.

Contribution

It presents a novel semantic communication method using LLM-based message condensation, importance-aware transmission, and a knowledge base to optimize bandwidth in embodied AI.

Findings

Achieves nearly 50% bandwidth reduction.

Maintains task performance with minimal degradation.

Demonstrates effectiveness through experimental results.

Abstract

The increasing deployment of agentic artificial intelligence (AI) systems has intensified the demand for efficient agent to agent communication, particularly over bandwidth limited wireless links. In embodied AI applications, agents must exchange task related information under strict latency and reliability constraints. Existing agent communication methods primarily focus on connectivity and protocol efficiency, but lack effective mechanisms to reduce physical layer transmission overhead while preserving task semantics.To address this challenge, this paper proposes a semantic agent communication framework that reduces communication overhead while maintaining task performance and shared understanding among agents. An LLM based semantic processor is first introduced to reorganize and condense agent generated messages by extracting task relevant semantic content. To cope with information loss introduced by aggressive message reduction, an importance-aware semantic transmission strategy is developed, which adaptively protects semantic components according to their task importance. Furthermore, a task specific knowledge base is incorporated as long term semantic memory to support recurring tasks and further reduce bandwidth consumption with minimal performance degradation. Experimental results and ablation studies demonstrate that the proposed framework achieves nearly 50% bandwidth reduction with negligible loss in task completion performance compared to conventional transmission schemes.