Contact State Estimation

Contact state estimation aims to accurately determine the presence, location, and nature of contact between a robot and its environment, crucial for tasks requiring manipulation and interaction. Current research focuses on developing robust methods using diverse data sources, including proprioceptive sensors (like IMUs), visual information, and force/torque measurements, often incorporating machine learning techniques such as neural networks and Kalman filters to process this data and estimate contact parameters. These advancements are improving robot control in various applications, from legged locomotion in unstructured terrains to dexterous manipulation of deformable objects, enhancing safety and performance in human-robot interaction and industrial automation.