Active Vision for Robot Manipulation

Active vision for robot manipulation studies how a robot can change its viewpoint or interact with objects to see what it needs for grasping and task execution, especially under occlusion or uncertainty. Methods include next-best-view planning, gaze control, and information-seeking actions, learned through reinforcement learning, imitation, or vision-language-action policies. Researchers evaluate these approaches in cluttered scenes, occluded-object tasks, and settings where viewpoints or environments change, often using simulation and real-robot trials. A central tension is that active sensing can improve robustness, but its benefits vary by task and must be balanced against extra movements, latency, data needs, and hardware complexity.