Denoising Diffusion Probabilistic Models (DDPMs) are powerful generative deep learning models that have been very successful at image generation, and, very recently, in path planning and control. In this paper, we investigate how to leverage the generalization and conditional sampling capabilities of DDPMs to generate complex paths for a robotic end effector. We show that training a DDPM with synthetic and low-quality demonstrations is sufficient for generating nontrivial paths reaching arbitrary targets and avoiding obstacles. Additionally, we investigate different strategies for conditional sampling combining classifier-free and classifier-guided approaches. Eventually, we deploy the DDPM in a receding-horizon control scheme to enhance its planning capabilities. The Denoising Diffusion Planner is experimentally validated through various experiments on a Franka Emika Panda robot.

翻译：去噪扩散概率模型（DDPMs）是强大的生成式深度学习模型，在图像生成领域已取得显著成功，近期更在路径规划与控制领域展现出潜力。本文研究如何利用DDPM的泛化能力与条件采样特性，为机器人末端执行器生成复杂路径。我们证明，使用合成数据与低质量演示训练DDPM足以生成能够抵达任意目标并规避障碍物的非平凡路径。此外，我们探索了结合无分类器引导与分类器引导策略的多种条件采样方法。最终，我们将DDPM部署于滚动时域控制框架中以增强其规划能力。通过Franka Emika Panda机器人上的系列实验，对去噪扩散规划器进行了实证验证。