Unpacking Failure Modes of Generative Policies: Runtime Monitoring of Consistency and Progress (2410.04640v2)

Abstract: Robot behavior policies trained via imitation learning are prone to failure under conditions that deviate from their training data. Thus, algorithms that monitor learned policies at test time and provide early warnings of failure are necessary to facilitate scalable deployment. We propose Sentinel, a runtime monitoring framework that splits the detection of failures into two complementary categories: 1) Erratic failures, which we detect using statistical measures of temporal action consistency, and 2) task progression failures, where we use Vision LLMs (VLMs) to detect when the policy confidently and consistently takes actions that do not solve the task. Our approach has two key strengths. First, because learned policies exhibit diverse failure modes, combining complementary detectors leads to significantly higher accuracy at failure detection. Second, using a statistical temporal action consistency measure ensures that we quickly detect when multimodal, generative policies exhibit erratic behavior at negligible computational cost. In contrast, we only use VLMs to detect failure modes that are less time-sensitive. We demonstrate our approach in the context of diffusion policies trained on robotic mobile manipulation domains in both simulation and the real world. By unifying temporal consistency detection and VLM runtime monitoring, Sentinel detects 18% more failures than using either of the two detectors alone and significantly outperforms baselines, thus highlighting the importance of assigning specialized detectors to complementary categories of failure. Qualitative results are made available at https://sites.google.com/stanford.edu/sentinel.

• The paper presents Sentinel, a dual-method framework that increases failure detection by 18% using STAC and VLM-driven video QA.
• It employs Statistical Temporal Action Consistency (STAC) to rapidly identify erratic behavior in generative policies in real time.
• Experimental results on simulated and real-world tasks show Sentinel’s robustness with a 97% detection rate of unknown failures.

Overview of "Unpacking Failure Modes of Generative Policies: Runtime Monitoring of Consistency and Progress"

This paper introduces Sentinel, a framework aimed at detecting failures in generative robot policies during deployment. The paper addresses the inherent challenges these policies face when encountering out-of-distribution (OOD) scenarios in real-world settings. By proposing a novel dual-category failure detection method, the authors present a comprehensive approach to runtime monitoring, tailored to the distinct characteristics of generative models, particularly diffusion policies.

Sentinel Framework

Sentinel is based on two complementary failure modes: erratic behavior and task progression failures. The detection strategy divides the task into:

1. Erratic Failures: These are detected through Statistical Temporal Action Consistency (STAC), measuring changes in the temporal distribution of actions. This method ensures rapid identification of erratic policy behavior without significant computational overhead.
2. Task Progression Failures: Vision LLMs (VLMs) are employed here to monitor task progress through a video question answering (QA) mechanism. These failures are less time-sensitive and require comprehensive context understanding.

Combining these two methodologies allows for improved detection accuracy. Specifically, Sentinel demonstrates an 18\% increase in failure detection compared to using either method alone.

Experimental Validation

The authors validate Sentinel on both simulated and real-world robotic tasks. Results highlight its superior performance in detecting various failure modes in high-dimensional and multimodal action spaces. Sentinel's ability to detect 97% of unknown failures in these environments underscores its robustness and potential applicability in diverse scenarios.

Simulations, including tasks like object manipulation, show that Sentinel outperforms alternative baselines in terms of true positive and negative rates. Notably, the paper provides thorough experimental comparisons, further establishing the framework's effectiveness.

Theoretical Contributions

The paper also presents a formal analysis of STAC, using conformity analysis to ensure low false positive rates. This aspect is crucial for maintaining reliability in high-stakes environments where false alarms can lead to unnecessary interventions.

Broader Implications and Future Directions

The introduction of Sentinel offers significant implications for deploying learned policies in real-world systems. By effectively monitoring generative policies, Sentinel improves the safety and reliability of autonomous systems, paving the way for broader adoption of advanced AI technologies in robotics.

Future work may explore integrating more failure categories and applying Sentinel to high-capacity policies. This direction could enhance the robustness of AI systems, further advancing their utility across various domains.

Overall, this paper presents a well-structured and technically sound approach to addressing a critical challenge in deploying generative models in uncertain environments. Sentinel's design and validation offer a meaningful step towards achieving reliable, failure-aware robotic systems.