Robot Safety

Robot safety research focuses on developing methods to ensure robots operate reliably and without causing harm, encompassing both physical safety and functional correctness. Current efforts concentrate on integrating robust control algorithms (e.g., control barrier functions, adversarial reinforcement learning) with advanced perception and uncertainty quantification techniques (e.g., Bayesian methods, diffusion models) to create safer and more adaptable robots. These advancements are crucial for expanding the deployment of robots into human-centered environments, improving human-robot collaboration, and fostering trust in autonomous systems across various applications.