Learning priors on trajectory distributions can help accelerate robot motion planning optimization. Given previously successful plans, learning trajectory generative models as priors for a new planning problem is highly desirable. Prior works propose several ways on utilizing this prior to bootstrapping the motion planning problem. Either sampling the prior for initializations or using the prior distribution in a maximum-a-posterior formulation for trajectory optimization. In this work, we propose learning diffusion models as priors. We then can sample directly from the posterior trajectory distribution conditioned on task goals, by leveraging the inverse denoising process of diffusion models. Furthermore, diffusion has been recently shown to effectively encode data multimodality in high-dimensional settings, which is particularly well-suited for large trajectory dataset. To demonstrate our method efficacy, we compare our proposed method - Motion Planning Diffusion - against several baselines in simulated planar robot and 7-dof robot arm manipulator environments. To assess the generalization capabilities of our method, we test it in environments with previously unseen obstacles. Our experiments show that diffusion models are strong priors to encode high-dimensional trajectory distributions of robot motions.

翻译：学习轨迹分布的先验知识有助于加速机器人运动规划优化。若能利用先前成功的规划案例，将轨迹生成模型作为新规划问题的先验知识将极具价值。既有研究提出了多种利用此类先验知识引导运动规划问题的方法：或从先验分布中采样初始解，或通过最大后验概率框架将先验分布融入轨迹优化。本研究提出将扩散模型作为先验知识进行学习，通过利用扩散模型的反向去噪过程，直接采样得到基于任务目标约束的后验轨迹分布。此外，最新研究表明扩散模型能有效编码高维数据中的多模态特征，这使其特别适用于大规模轨迹数据集。为验证方法有效性，我们将提出的运动规划扩散方法与多种基线方法在模拟平面机器人和七自由度机械臂环境中进行对比。为评估方法泛化能力，我们在含有未知障碍物的环境中进行测试。实验结果表明，扩散模型是编码机器人高维轨迹分布的有效先验框架。