Tactile Feedback

Tactile feedback research aims to equip robots with a sense of touch, enabling them to interact with their environment more dexterously and robustly. Current research focuses on developing advanced tactile sensors, integrating them with control algorithms (like reinforcement learning and active inference), and applying these systems to diverse manipulation tasks, including insertion, grasping, and object manipulation in unstructured environments. This work is significant because it bridges the gap between robotic manipulation and human-like dexterity, with potential applications in prosthetics, surgery, manufacturing, and human-robot collaboration. The use of deep learning models, particularly neural networks and transformers, is prominent in processing tactile data and learning control policies.