Contact Rich Manipulation Task

Contact-rich manipulation, focusing on robotic tasks requiring precise interaction and contact with objects, aims to enable robots to perform dexterous actions like insertion and assembly. Current research emphasizes learning robust control policies from diverse data sources, including visual, tactile, and force-torque information, often employing imitation learning, model predictive control, and deep learning architectures like transformers and diffusion models. These advancements are crucial for improving robot dexterity and safety in real-world applications, such as industrial automation and assistive robotics, by enabling more reliable and adaptable manipulation in unstructured environments.