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Related Experiment Video

Updated: May 30, 2026

Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy
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Assessing Vision-Language Models for Failure Detection in Robotic Manipulation.

Md Sameer Iqbal Chowdhury, Tsz-Chiu Au

    IEEE Pulse
    |April 14, 2026
    PubMed
    Summary

    Vision-language models (VLMs) can now detect their own limitations in robotic tasks. This ensures safety by enabling VLMs to request human assistance when needed, preventing failures.

    Area of Science:

    • Robotics
    • Artificial Intelligence
    • Computer Vision

    Background:

    • Vision-language models (VLMs) show promise for robotics.
    • Deployment is limited by performance and safety concerns in critical applications.
    • Robots must recognize their limitations to prevent failures.

    Purpose of the Study:

    • To systematically study VLMs for robotic failure detection.
    • To develop a decision-making process for VLMs to assess task completion capability.
    • To enable VLMs to request human intervention when necessary.

    Main Methods:

    • Evaluated six VLM architectures on real-world robotic trajectories.
    • Developed and tested a VLM decision-making process for failure detection.
    • Focused on safety-critical manipulation tasks.

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    646

    Main Results:

    • VLMs can be effectively used for robotic failure detection.
    • A decision-making process allows VLMs to identify task limitations.
    • Well-calibrated VLMs can reliably signal when human assistance is required.

    Conclusions:

    • VLMs can be trustworthy partners in robotic systems.
    • Knowing when to ask for help is crucial for VLM safety and reliability.
    • This work advances the safe deployment of VLMs in robotics.