Proprioceptive Sensing

Proprioceptive sensing focuses on a robot's internal awareness of its body position, movement, and forces, crucial for safe and effective interaction with the environment. Current research emphasizes integrating proprioceptive data (from sensors like IMUs, encoders, and force/torque sensors) with exteroceptive data (vision, lidar) using various methods, including Kalman filters, neural networks (CNNs, GRUs), and probabilistic models, to improve navigation, manipulation, and terrain classification. This enhanced self-awareness is vital for advancing robotics in diverse applications, from legged robots navigating challenging terrains to soft robots performing delicate manipulation tasks, ultimately leading to more robust and adaptable autonomous systems.