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arXiv Machine Learning By Carl Qi, Xiaojie Wang, Silong Yong, Stephen Sheng, Huitan Mao, Sriram Srinivasan, Manikantan Nambi, Amy Zhang, Yesh Dattatreya

Self-Refining Vision Language Model for Robotic Failure Detection and Reasoning

Read the original on arXiv Machine Learning →

The paper introduces ARMOR, an Adaptive Round-based Multi-task Model designed for robotic failure detection and reasoning. It treats detection and reasoning as a self‑refinement process, iteratively predicting outcomes and natural language explanations conditioned on prior outputs. Training leverages both large sparse binary labels and small rich reasoning annotations through offline and online imitation learning, while inference uses multiple refinement trajectories and a self‑certainty metric to select the best prediction.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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