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New FailBench benchmark reveals VLM unreliability in robot task success evaluation

By PulseAugur Editorial · [1 sources] ·

A new benchmark called FailBench has been developed to assess the reliability of vision-language models (VLMs) in judging robot task success. The benchmark, comprising 2,197 manipulation attempts, reveals that even the best performing VLM achieved only 0.77 balanced accuracy in detecting robot failures. Models fine-tuned for failure detection performed worse than general-purpose VLMs, and their performance significantly degraded on tasks requiring fine-grained visual evidence, such as assembly. Researchers found a bias towards predicting success in ambiguous situations, though input-level interventions like spatial localization showed potential for improvement. AI

IMPACT Highlights limitations in current VLMs for critical robotics applications, suggesting a need for improved robustness and specialized training for failure detection.

RANK_REASON The cluster contains an academic paper introducing a new benchmark and evaluation results. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New FailBench benchmark reveals VLM unreliability in robot task success evaluation

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The cluster contains an academic paper introducing a new benchmark and evaluation results. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Zaruhi Navasardyan, Tatul Danielyan, Hrant Davtyan ·

    FailBench: How Reliable are VLMs at Judging Robot Task Success?

    arXiv:2609.03611v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) are increasingly used to evaluate robot manipulation outcomes, but existing benchmarks offer limited evidence of cross-domain generalization. We introduce FailBench, a benchmark for robot failure dete…

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