Exteroceptive Perception
Exteroceptive perception, the ability of a system to sense its external environment, is a crucial area of research in robotics and autonomous systems. Current efforts focus on improving the robustness and efficiency of exteroceptive sensing, particularly in challenging conditions, using various approaches including deep reinforcement learning (DRL) with variational autoencoders (VAEs) for data compression, attention-based recurrent neural networks for multimodal data fusion, and central pattern generators (CPGs) integrated with DRL for locomotion control. These advancements are driving progress in areas such as autonomous navigation, human-robot interaction, and manipulation of deformable objects, with significant implications for the development of more capable and adaptable robots.