End to End Perception

End-to-end perception in robotics and autonomous systems aims to directly process raw sensor data (e.g., camera, LiDAR, radar) to produce actionable information, bypassing intermediate steps like manual feature extraction. Current research emphasizes deep learning architectures, often employing multi-task learning and sensor fusion techniques within a single network to simultaneously perform tasks such as object detection, segmentation, and mapping. This approach promises improved efficiency, robustness, and accuracy compared to traditional, modular perception systems, with significant implications for autonomous vehicles, legged robots, and other applications requiring reliable real-time environmental understanding.