Tactile Based

Tactile-based robotics focuses on enabling robots to interact with their environment using touch, mimicking human dexterity and manipulation skills without relying solely on vision. Current research emphasizes developing accurate tactile sensors and simulations, often employing machine learning techniques like reinforcement learning and neural networks (including convolutional and graph neural networks) to process sensor data and control robot actions. This field is crucial for advancing robotic manipulation in unstructured environments, improving safety in human-robot collaboration, and enabling robots to perform complex tasks requiring fine motor control and force feedback.