EmCoop: A Framework and Benchmark for Embodied Cooperation Among LLM Agents
Hanqing Yang, Shiyu Chen, Narjes Nourzad, Marie Siew, Jingdi Chen, Carlee Joe-Wong

TL;DR
EmCoop introduces a comprehensive benchmark framework for analyzing cooperation among LLM-based embodied agents, enabling detailed study of their interaction dynamics and collaboration quality in complex multi-agent tasks.
Contribution
The paper presents EmCoop, a novel benchmark separating high-level cognition from embodied interaction, with process-level metrics for cooperation analysis in multi-agent systems.
Findings
Framework supports arbitrary team sizes and communication topologies.
Enables systematic analysis of cooperation dynamics.
Facilitates diagnosis of collaboration failures.
Abstract
Real-world scenarios increasingly require multiple embodied agents to collaborate in dynamic environments under embodied constraints, as many tasks exceed the capabilities of any single agent. Recent advances in large language models (LLMs) enable high-level cognitive coordination through reasoning, planning, and natural language communication. However, fine-grained analyses of how such collaboration emerges, unfolds, and contributes to task success in embodied multi-agent systems are difficult to conduct with existing benchmarks. In this paper, we introduce EmCoop, a benchmark framework for studying cooperation in LLM-based embodied multi-agent systems. Our framework separates a high-level cognitive layer from a low-level embodied interaction layer, allowing us to characterize agent cooperation through their interleaved dynamics over time. Given a cooperation-constrained embodied task,…
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Taxonomy
TopicsLanguage and cultural evolution · Action Observation and Synchronization · Social Robot Interaction and HRI
