Robots can learn intent-aware camera control from passive human video by mining supervision pairs of language descriptions, observation changes, and target poses—turning everyday egocentric footage into training data for active perception.
This paper tackles language-conditioned camera motion for robots by learning from egocentric video. Given an image and natural language intent, LIME predicts the next camera pose by combining observation-gain prediction with flow-matching, enabling robots to actively position cameras for inspection, occlusion handling, or user-intent-driven viewing.