AgentComm-Bench: Stress-Testing Cooperative Embodied AI Under Latency, Packet Loss, and Bandwidth Collapse
arXiv:2603.20285v1 Announce Type: new Abstract: Cooperative multi-agent methods for embodied AI are almost universally evaluated under idealized communication: zero latency, no packet loss, and unlimited bandwidth. Real-world deployment on robots with wireless links, autonomous vehicles on congested networks, or drone swarms in contested spectrum offers no such guarantees. We introduce AgentComm-Bench, a benchmark suite and evaluation protocol that systematically stress-tests cooperative embodied AI under six communication impairment dimensions: latency, packet loss, bandwidth collapse, asynchronous updates, stale memory, and conflicting sensor evidence. AgentComm-Bench spans three task families: cooperative perception, multi-agent waypoint navigation, and cooperative zone search, and evaluates five communication strategies, including a lightweight method we propose based on redundant message coding with staleness-aware fusion. Our experiments reveal that communication-dependent tasks
arXiv:2603.20285v1 Announce Type: new Abstract: Cooperative multi-agent methods for embodied AI are almost universally evaluated under idealized communication: zero latency, no packet loss, and unlimited bandwidth. Real-world deployment on robots with wireless links, autonomous vehicles on congested networks, or drone swarms in contested spectrum offers no such guarantees. We introduce AgentComm-Bench, a benchmark suite and evaluation protocol that systematically stress-tests cooperative embodied AI under six communication impairment dimensions: latency, packet loss, bandwidth collapse, asynchronous updates, stale memory, and conflicting sensor evidence. AgentComm-Bench spans three task families: cooperative perception, multi-agent waypoint navigation, and cooperative zone search, and evaluates five communication strategies, including a lightweight method we propose based on redundant message coding with staleness-aware fusion. Our experiments reveal that communication-dependent tasks degrade catastrophically: stale memory and bandwidth collapse cause over 96% performance drops in navigation, while content corruption (stale or conflicting data) reduces perception F1 by over 85%. Vulnerability depends on the interaction between impairment type and task design; perception fusion is robust to packet loss but amplifies corrupted data. Redundant message coding more than doubles navigation performance under 80% packet loss. We release AgentComm-Bench as a practical evaluation protocol and recommend that cooperative embodied AI work report performance under multiple impairment conditions.
Executive Summary
This article introduces AgentComm-Bench, a benchmark suite designed to evaluate cooperative embodied AI under various communication impairments, such as latency, packet loss, and bandwidth collapse. The authors highlight the significance of realistic testing in the real-world deployment of embodied AI systems, which often face various communication challenges. Through experiments, they demonstrate the catastrophic impact of communication-dependent tasks under different impairment conditions. The study provides valuable insights into the vulnerability of embodied AI systems and recommends that researchers report performance under multiple impairment conditions. This article is a crucial step towards developing more robust and reliable embodied AI systems.
Key Points
- ▸ AgentComm-Bench is a benchmark suite for evaluating cooperative embodied AI under realistic communication conditions.
- ▸ The authors highlight the importance of realistic testing in embodied AI development.
- ▸ Experiments demonstrate the catastrophic impact of communication-dependent tasks under different impairment conditions.
Merits
Comprehensive evaluation framework
AgentComm-Bench provides a comprehensive framework for evaluating cooperative embodied AI under various communication impairments, making it a valuable resource for researchers and developers.
Realistic testing
The study emphasizes the importance of realistic testing in embodied AI development, which is crucial for ensuring the reliability and robustness of these systems.
Practical recommendations
The authors provide practical recommendations for improving the performance of embodied AI systems under different impairment conditions.
Demerits
Limited scope
The study focuses on cooperative embodied AI systems and may not be directly applicable to other types of AI systems or applications.
Impairment conditions
The authors consider a limited set of impairment conditions, and further research may be needed to evaluate the performance of embodied AI systems under additional impairment scenarios.
Task design
The study highlights the importance of task design in affecting the vulnerability of embodied AI systems to communication impairments, but further research is needed to explore this relationship in more detail.
Expert Commentary
This article represents a significant step forward in the development of cooperative embodied AI systems. By emphasizing the importance of realistic testing and providing a comprehensive evaluation framework, the authors have made a valuable contribution to the field. However, further research is needed to explore the relationship between task design and system vulnerability and to evaluate the performance of embodied AI systems under additional impairment conditions. The study's findings have significant implications for the development of autonomous vehicles, drone swarms, and robotics, and policymakers should consider these implications when developing regulations and investing in research and development.
Recommendations
- ✓ Researchers should extend AgentComm-Bench to include additional impairment conditions and evaluate the performance of embodied AI systems under these conditions.
- ✓ Developers should prioritize the creation of more robust and reliable embodied AI systems that can operate effectively under various impairment conditions.
Sources
Original: arXiv - cs.AI
