基于先验灵巧抓取位姿知识的灵巧操作

中文摘要 AI 辅助

灵巧操作在近期研究中受到广泛关注。现有研究主要集中于使用强化学习方法来解决手部运动的大量自由度问题。然而，这些方法通常效率低且准确度差。在本研究中，我们提出了一种新颖的强化学习方法，利用先验灵巧抓取位姿知识来同时提升效率和准确度。与以往工作总是让机械手保持固定的灵巧抓取位姿不同，我们将操作过程解耦为两个独立阶段：首先，我们生成针对物体功能部位的灵巧抓取位姿；随后，我们采用强化学习全面探索环境。我们的发现表明，大部分学习时间消耗在寻找合适的初始位置和选择最佳操作视角上。实验结果表明，在四个不同任务中，学习效率和成功率均有显著提升。

英文摘要

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.

发表机构

• Shanghai Jiao Tong University（上海交通大学）

机构由 AI 辅助整理，请以论文原文为准。