Multi Fingered Hand

Multi-fingered robotic hands aim to replicate the dexterity of human hands for complex manipulation tasks, focusing on robust grasping and in-hand manipulation of diverse objects in unstructured environments. Current research emphasizes developing efficient representations for grasp planning and control, often employing deep learning models like graph convolutional networks and autoregressive models, alongside advanced control strategies such as model predictive control and reinforcement learning. These advancements are crucial for improving robotic manipulation in various fields, including assistive robotics, manufacturing, and human-robot interaction, by enabling robots to handle a wider range of objects and tasks with greater precision and adaptability.