Perception
Related papers: 20
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Perception in robotics and AI refers to the set of processes and technologies by which a system acquires, interprets, and makes sense of information from its environment through sensors. This spans a broad range of modalities, including vision (cameras, depth sensors), touch (tactile and force sensors), and proprioception (internal state awareness), enabling robots to build coherent models of the world around them. In practice, perception underpins nearly every meaningful robotic capability: a mobile robot uses camera and LiDAR data to localize itself and map surroundings via SLAM, a manipulation system relies on tactile feedback to grasp objects securely, and a legged robot processes terrain geometry to walk over uneven ground. Deep learning approaches, such as convolutional neural networks, have dramatically improved the accuracy and robustness of perceptual pipelines, and end-to-end frameworks increasingly integrate perception directly with control. Perception matters because without reliable sensing and interpretation, autonomous decision-making is impossible. It is the foundational layer connecting a robot's physical embodiment to its cognitive processes, directly determining how effectively the system can act safely and adaptively in complex, real-world environments.
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