AgentComm-Bench: Stress-Testing Cooperative Embodied AI Under Latency, Packet Loss, and Bandwidth Collapse
Aayam Bansal, Ishaan Gangwani

TL;DR
This paper introduces AgentComm-Bench, a comprehensive benchmark suite for stress-testing cooperative embodied AI systems under various realistic communication impairments, revealing critical vulnerabilities and proposing robust strategies.
Contribution
It presents a new benchmark and evaluation protocol for testing cooperative embodied AI under communication impairments, including a novel redundant message coding method.
Findings
Performance drops over 96% under bandwidth collapse in navigation tasks.
Content corruption reduces perception F1 score by over 85%.
Redundant message coding more than doubles navigation performance under 80% packet loss.
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…
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Taxonomy
TopicsSocial Robot Interaction and HRI · UAV Applications and Optimization · Opportunistic and Delay-Tolerant Networks
