Advancing Grasping Mechanics

This chapter explores the transition from representation models to decision models in robotic grasping, emphasizing the need for reduced training data through low-dimensional representations. It details efficient sampling methods and the training process using the GRASPNet dataset to improve generalization across various objects. Additionally, it discusses the grasp taxonomy and advanced techniques for enhancing the learning efficiency of robotic hands while considering real-world application challenges.