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This paper introduces a novel resilience evaluation framework for Embodied Agents Systems (EAS) that emphasizes the importance of resilience鈥攄efined as the ability to recover, stabilize, and extend under perturbations鈥攐ver traditional outcome-centric metrics. By developing a comprehensive suite of resilience metrics, including Rebound, Stability, and Graceful Extensibility, the authors provide a systematic approach to assess and optimize EAS performance across diverse tasks. The findings highlight significant process-level distinctions, revealing that optimizing for resilience can reduce recovery costs and enhance stability, ultimately informing deployment-specific configurations for EASs.
Resilience evaluation reveals hidden process-level distinctions in EAS performance, showing that optimizing for recovery can significantly reduce costs and improve stability.
Embodied Agents System (EAS) are increasingly deployed in open-world physical domains, where reliability directly dictates deployment quality and human-agent trust. However, existing evaluations rely on outcome-centric metrics as success rate or safety scores that collapse diverse execution trajectories into coarse scores, obscuring the dynamic processes underlying agent behavior. Therefore, they ignore a critical property of EAS -- which we define as the Resilience -- that reflects how EASs recover, stabilize, and extend under perturbations and across iterative updates. The lack of resilience is particularly critical in open-world environments due to continuous unexpected disruptions, thus directly affecting the quality of EAS deployment. To address this problem, we gain insight from the resilience-engineering concepts to EAS groundings and propose a novel resilience evaluation framework that can be flexibly applied to any EAS. Specifically, we define the first comprehensive resilience metrics suite for EASs system that exposes Rebound, Stability, and Graceful Extensibility across embodied tasks execution, providing a practical grounding for EAS resilience analysis. We further implement the resilience evaluation layer that transforms execution process into assessments for diagnosis and optimization. Across 400 household tasks with 10 EAS, we reveal the process-level distinction hidden by outcome metrics, including recovery cost differences among successful episodes ($\Delta C_{rec}=25.2$), increased instability and task-family degradation. Metrics-guided optimizations reduce recovery cost and increase stability, graceful extensibility completion, showing the diagnostic effect of resilience evaluation. Our results reveal a trade-off among resilience characteristics, suggesting that a resilient EAS construction should be configured according to deployment-specific requirements.
