Safety Evaluation of VLA Models
VLA: Vision-Language-Action
Overview
This field tests whether robots and driving agents can interpret instructions and act safely in interactive environments. Benchmarks use matched safe and unsafe scenarios, trajectory-level safety rules, counterfactual tests, and simulation-to-physical evaluation to separate safe behavior from task completion. Methods include adversarial red teaming, runtime action screening and abstention, semantic guards, robustness checks, and safety-aware training. Strong hazard recognition or task success often fails to ensure safe execution, leaving open challenges in cross-domain generalization, standardized metrics, and balancing safety with useful performance.
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