从演示中学习用于柔顺富接触操作的扩散策略
Learning Diffusion Policies from Demonstrations For Compliant Contact-rich Manipulation
AI总结：
针对富接触操作中稳定接触与力控制难题，提出DIPCOM扩散策略框架，从演示学习末端位姿与刚度调节，并通过真实任务验证其优势。
摘要：
机器人在执行重复性或危险性任务方面具有巨大前景，但要实现类似人类的灵巧性，尤其是在富接触和动态环境中，仍然具有挑战性。依赖位置或速度控制的刚性机器人，在力密集型任务中往往难以维持稳定接触并施加一致的力。从演示中学习已成为一种解决方案，但需要复杂动作的任务（例如粉末研磨）呈现出独特困难。本文介绍了用于柔顺操作的扩散策略（Diffusion Policies For Compliant Manipulation，DIPCOM），这是一种专为柔顺控制任务设计的新型基于扩散的框架。通过利用生成式扩散模型，我们开发了一种策略，用于预测笛卡尔末端执行器位姿并调整机械臂刚度，以维持所需的力。我们的方法通过多模态分布建模增强了力控制，改进了扩散策略在柔顺控制中的集成，并通过在真实世界任务中证明其有效性扩展了我们以往的工作。我们给出了该框架与现有方法的详细比较，强调了部署基于扩散的柔顺控制的优势和最佳实践。
英文摘要：
Robots hold great promise for performing repetitive or hazardous tasks, but achieving human-like dexterity, especially in contact-rich and dynamic environments, remains challenging. Rigid robots, which rely on position or velocity control, often struggle with maintaining stable contact and applying consistent force in force-intensive tasks. Learning from Demonstration has emerged as a solution, but tasks requiring intricate maneuvers, such as powder grinding, present unique difficulties. This paper introduces Diffusion Policies For Compliant Manipulation (DIPCOM), a novel diffusion-based framework designed for compliant control tasks. By leveraging generative diffusion models, we develop a policy that predicts Cartesian end-effector poses and adjusts arm stiffness to maintain the necessary force. Our approach enhances force control through multimodal distribution modeling, improves the integration of diffusion policies in compliance control, and extends our previous work by demonstrating its effectiveness in real-world tasks. We present a detailed comparison between our framework and existing methods, highlighting the advantages and best practices for deploying diffusion-based compliance control.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
