Vision-Based Robot Control

The field studies how robots can use camera images to choose and revise actions, allowing them to operate when precise maps or external tracking are unavailable. Approaches range from image-based visual servoing and model-predictive control to imitation learning, reinforcement learning, and vision-language-action or world-model policies. These methods are tested in tasks such as object manipulation, legged locomotion, aerial navigation, and surgical camera control, using simulation benchmarks and real-robot trials. A central challenge is making policies transfer across cameras, robots, and environments while remaining responsive and safe under occlusion, latency, and changing conditions.