# ShieldVLA

ShieldVLA is a safety-aligned fine-tuning framework for vision-language-action (VLA) models that learns a model-free Hamilton-Jacobi reachability value function from visual observations as a safety critic, uses that critic to gate policy optimization into reward maximization inside feasible regions and recovery near unsafe states, and replaces manual per-step cost labels with rubric-based VLM safety scores; across five navigation and manipulation benchmarks (Dubins-VL, TurtleBot-Nav, Safety-CHORES Nav/Fetch, Franka-Reach) and multiple VLA backbones it reduces cumulative safety cost by 57% on average and improves task success rate by 0.13 over SafeVLA.

ShieldVLA is a safety-aligned fine-tuning framework for vision-language-action (VLA) models that learns a model-free Hamilton-Jacobi reachability value function from visual observations as a safety critic, uses that critic to gate policy optimization into reward maximization inside feasible regions and recovery near unsafe states, and replaces manual per-step cost labels with rubric-based VLM safety scores; across five navigation and manipulation benchmarks (Dubins-VL, TurtleBot-Nav, Safety-CHORES Nav/Fetch, Franka-Reach) and multiple VLA backbones it reduces cumulative safety cost by 57% on average and improves task success rate by 0.13 over SafeVLA.