FailBench: How Reliable are VLMs at Judging Robot Task Success?
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 detection comprising 2,197 manipulation attempts across 14 public sources (12 real-world, 2 simulated). In FailBench, 75% of failures occur naturally, and six real-world sources come from non-failure-detection datasets. Evaluating 13 VLM-based detectors, we find the best model achieves only 0.77 mean balanced accuracy. Notably, models fine-tuned for failure detection consistently underperform general-purpose VLMs and their own pretrained baselines. Performance depend...
| Authors | Zaruhi Navasardyan, Tatul Danielyan, Hrant Davtyan |
|---|---|
| Primary category | cs.RO |
| Published | 3 September 2026 |
| Last updated at source | 3 September 2026 |
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