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New framework detects robot execution failures using trajectory data

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new framework called Hide-and-Seek to improve the reliability of robots using Vision-Language-Action (VLA) models. This method detects execution failures by identifying specific actions that indicate a problem, without requiring detailed step-by-step annotations. By using contrastive learning on trajectory-level data, Hide-and-Seek can pinpoint failure signals and offers a good balance between accuracy and timeliness for real-world robotic applications. AI

IMPACT Enhances the reliability of embodied AI systems by enabling more robust failure detection during robotic task execution.

RANK_REASON The cluster contains an academic paper detailing a new framework for failure detection in VLA models.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New framework detects robot execution failures using trajectory data

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Research
The cluster contains an academic paper detailing a new framework for failure detection in VLA models.
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2 independent sources
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paper, safety
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120 days old
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Seongheon Park, Wendi Li, Changdae Oh, Samuel Yeh, Zsolt Kira, Michael Hagenow, Sharon Li ·

    Hide-and-Seek in Trajectories: Discovering Failure Signals for VLA Runtime Monitoring

    arXiv:2605.30834v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models enable robots to follow natural language instructions and generalize across diverse tasks, but they remain vulnerable to execution failures that compromise reliability in real-world deployment. …

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Hide-and-Seek in Trajectories: Discovering Failure Signals for VLA Runtime Monitoring

    Hide-and-Seek framework detects robot execution failures in vision-language-action models by localizing failure-indicative actions through contrastive learning from trajectory-level supervision without step-level annotations.

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