Robot Safety

Robot safety concerns preventing collisions, injury, unsafe contact, and unauthorized actions while robots operate in homes, workplaces, hospitals, and shared spaces. Common approaches include control barrier functions and reachability for formal safety constraints, runtime shields and backup controllers, and safe reinforcement learning that balances task reward with risk. As vision-language-action models and other learned policies enter robot control, researchers add perception-aware filters, rule-based authorization, adversarial testing, and benchmarks that measure safety throughout execution rather than task success alone. A central challenge is achieving useful performance under uncertain dynamics and imperfect sensing: guarantees depend on assumptions, interventions can be overly conservative, and success in simulation may not predict safe real-world behavior.