用扩散模型生成柔性操作策略，实现高精度力控操作。
Learning Diffusion Policies from Demonstrations For Compliant Contact-rich Manipulation
- 基于扩散模型预测末端位姿并动态调节臂部刚度
- 在真实任务中实现稳定接触与一致力控表现
- 适合需要精细力控的复杂操作场景
机器人在执行重复性或危险任务方面潜力巨大，但在接触密集且动态变化的环境中实现类人灵巧性仍具挑战。传统依赖位置或速度控制的刚性机器人难以在高力要求任务中保持稳定接触和一致施力。基于示范学习成为解决方案，但如粉末研磨等复杂操作仍存在困难。本文提出一种基于扩散模型的柔性操控框架DIPCOM，利用生成式扩散模型预测笛卡尔空间末端位姿，并动态调整机械臂刚度以维持所需作用力。该方法通过多模态分布建模提升力控性能，改进了扩散策略在柔顺控制中的融合方式，并在真实任务中验证了其有效性。我们详细对比了现有方法，明确了基于扩散模型的柔顺控制部署优势与最佳实践。
原文摘要 · Abstract (English)
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.
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