Deep Learning Based Perception

Deep learning-based perception aims to enable machines to understand their environment using artificial neural networks, primarily focusing on robust and reliable interpretation of sensor data (e.g., camera images, LiDAR point clouds). Current research emphasizes improving the accuracy and reliability of these perception models, particularly addressing challenges like the "sim-to-real gap" through contrastive learning and unsupervised domain adaptation techniques, and enhancing their safety and robustness through uncertainty estimation and verifiable control strategies. This field is crucial for advancing autonomous systems in various domains, from autonomous driving and robotics to animal monitoring, by providing a foundation for safe and effective decision-making based on accurate environmental understanding.