Dexterous Manipulation Based on Prior Dexterous Grasp Pose Knowledge
This work addresses the problem of inefficient and inaccurate robotic dexterous manipulation, which is incremental as it builds on existing reinforcement learning approaches by incorporating prior knowledge.
The paper tackles the low efficiency and accuracy of reinforcement learning for dexterous manipulation by introducing a method that leverages prior grasp pose knowledge, decoupling the process into grasp generation and exploration phases, resulting in significant improvements in learning efficiency and success rates across four tasks.
Dexterous manipulation has received considerable attention in recent research. Predominantly, existing studies have concentrated on reinforcement learning methods to address the substantial degrees of freedom in hand movements. Nonetheless, these methods typically suffer from low efficiency and accuracy. In this work, we introduce a novel reinforcement learning approach that leverages prior dexterous grasp pose knowledge to enhance both efficiency and accuracy. Unlike previous work, they always make the robotic hand go with a fixed dexterous grasp pose, We decouple the manipulation process into two distinct phases: initially, we generate a dexterous grasp pose targeting the functional part of the object; after that, we employ reinforcement learning to comprehensively explore the environment. Our findings suggest that the majority of learning time is expended in identifying the appropriate initial position and selecting the optimal manipulation viewpoint. Experimental results demonstrate significant improvements in learning efficiency and success rates across four distinct tasks.