aiqbench analysis
ARMOR is a research preprint that trains a vision-language model to detect robotic failures and explain them in natural language, using a mix of abundant sparse binary labels and scarce rich reasoning annotations. Its core idea is iterative self-refinement at inference, with the most confident of several trajectories selected by a self-certainty score. The reported gains (up to 30% on detection rate, up to 100% on reasoning fuzzy-match scores) are self-reported against prior baselines, so readers should check the benchmarks, the size of the rich-annotation set, and how the self-certainty metric holds up outside the test environments.
Self-Refining Vision Language Model for Robotic Failure Detection and Reasoning
arXiv:2602.12405v2 Announce Type: replace-cross Abstract: 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 ARMOR: Adaptive Round-based Multi-task mOdel for Robotic failure detection and reasoning. We formulate detection and reasoning as a multi-task self-refinement process, where the model iteratively predicts detection outcomes and natural language reasoning conditioned on past outputs. During training, ARMOR learns from heterogeneous supervision - large-scale sparse binary labels and small-scale rich reasoning annotations - optimized via a combination of offline and online imitation learning. At inference time, ARMOR generates multiple refinement trajectories and selects the most confident prediction via a self-certainty metric. Experiments across diverse environments show that ARMOR achieves state-of-the-art performance by improving over the previous approaches by up to 30% on failure detection rate and up to 100% in reasoning measured through LLM fuzzy match score, demonstrating robustness to heterogeneous supervision and open-ended reasoning beyond predefined failure modes. We provide additional visualizations on our website: https://sites.google.com/utexas.edu/armor
Abstract: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 ARMOR: Adaptive Round-based Multi-task mOdel for Robotic failure detection and reasoning. We formulate detection and reasoning as a multi-task self-refinement process, where the model iteratively predicts detection outcomes and natural language reasoning conditioned on past outputs. During training, ARMOR learns from heterogeneous supervision - large-scale sparse binary labels and small-scale rich reasoning annotations - optimized via a combination of offline and online imitation learning. At inference time, ARMOR generates multiple refinement trajectories and selects the most confident prediction via a self-certainty metric. Experiments across diverse environments show that ARMOR achieves state-of-the-art performance by improving over the previous approaches by up to 30% on failure detection rate and up to 100% in reasoning measured through LLM fuzzy match score, demonstrating robustness to heterogeneous supervision and open-ended reasoning beyond predefined failure modes. We provide additional visualizations on our website: this https URL
Robotics (cs.RO); Machine Learning (cs.LG)
arXiv:2602.12405 [cs.RO]
(or arXiv:2602.12405v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2602.12405 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 291 words
