<ul><li>Dexterous manipulation has been a focus of recent research.</li><li>Existing studies have primarily used reinforcement learning methods for hand movements, but these methods are often inefficient and inaccurate.</li><li>This work introduces a novel reinforcement learning approach that utilizes prior dexterous grasp pose knowledge to improve efficiency and accuracy.</li><li>The manipulation process is divided into two phases: generating a dexterous grasp pose targeting the functional part of the object, and using reinforcement learning to explore the environment.</li></ul>

Dexterous Manipulation Based on Prior Dexterous Grasp Pose Knowledge

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