Active Perception
Overview
Active perception studies how an agent can improve what it knows by moving a camera or robot, selecting video frames or image regions, or probing an object or person. Common approaches use information gain and belief models, next-best-view planning, learned policies, and vision-language agents that gather evidence in multiple steps; maps and memory help combine observations over time. These methods support tasks such as robot navigation and manipulation, 3D mapping, and visual question answering, where a poorly chosen viewpoint or missing detail can lead to errors. Benchmarks such as ESI-Bench and ActiveArena show that active observation can help, but agents still struggle with reliable memory, long-horizon tasks, transfer to new settings, and balancing information gathering against time, energy, and safety.
Latest papers
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