The field of robotic manipulation is moving towards the development of more advanced tactile sensing systems, enabling robots to better understand and interact with their environment. Recent research has focused on creating neuromorphic tactile sensors, which mimic the human sense of touch, and developing new algorithms for processing and interpreting tactile data. These advancements have led to improved performance in tasks such as object recognition, force estimation, and texture classification. Notably, the use of exploratory movement strategies and distributed tactile sensing has shown promise in enhancing robotic environmental interaction and dexterous in-hand manipulation.

Some noteworthy papers in this area include: The Neuromorphic Incipient Slip Detection System, which presents a system with stable and responsive incipient slip detection capability. The MoiréTac sensor, which generates dense interference patterns for simultaneous 6-axis force/torque measurement and visual perception. The exUMI system, which introduces a tactile robot learning system with hardware and algorithm innovations for efficient data collection and representation.