Self-refining vision language model for robotic failure detection and reasoning
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- Source
- amazon.science
Reasoning about failures is crucial for building reliable and trustworthy robotic systems. Prior approaches either treat failure reasoning as a closed-set classification problem or assume access to ample human annotations. Failures in the real world are typically subtle, combinatorial, and difficult to enumerate, whereas rich reasoning labels are expensive to acquire. We address this problem by introducing
Original report
Self-refining vision language model for robotic failure detection and reasoning
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