ShieldVLA proposes feasibility-aware safety alignment for vision-language-action models
A new arXiv paper introduces ShieldVLA, a method for safety alignment in vision-language-action models used in robotic manipulation and navigation. The authors argue that existing fine-tuning approaches, which largely depend on Lagrangian optimization, offer only limited safety guarantees. The work instead frames safety as a feasibility-aware alignment problem.