SafeVLA-Bench: A Benchmark for the Success-Safety Gap in Vision-Language-Action Models

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Chinese explanation / 中文解读

中文解读待补充：本站会优先为 VLA、具身智能、人形机器人控制、机器人操作等高价值论文补充中文说明。

Original abstract

Vision-language-action (VLA) benchmarks measure whether a policy completes a requested manipulation task, but binary success can hide safety-relevant trajectory behavior: reaching the goal while applying excessive contact, disturbing bystander objects, destabilizing the held object, or entering robot self-contact. We present SafeVLA-Bench, a post-hoc safety-evaluation framework for existing simulator-based VLA benchmarks. It formalizes task-aware safety requirements as Signal Temporal Logic (STL) specifications and reports native success with two unsafe-success metrics: Succ-But-Unsafe (SBU), the fraction of rollouts that both succeed and violate safety, and Violation Severity Index (VSI), a bounded worst-violation depth score. We instantiate SafeVLA-Bench on LIBERO and RoboCasa-365, evaluating nine policy-benchmark entries across tabletop and kitchen manipulation tasks. High task success does not imply safe execution: high-SR tabletop baselines still leave 13 to 15 percent unsafe-episode rates,and 36 to 56 percent of successful RoboCasa-365 rollouts violate at least one active safety clause. Project page: https://safevla.org.

Links and sources

• Official / arXiv page
• PDF from original source

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