运动规划扩散：使用扩散模型学习与规划机器人运动
Motion Planning Diffusion: Learning and Planning of Robot Motions with Diffusion Models
AI总结：
本文提出将扩散模型作为先验，利用其逆去噪过程直接从以任务目标为条件的后验轨迹分布中采样，以加速机器人运动规划，实验证明该方法能有效编码高维轨迹分布并具备良好的泛化能力。
摘要：
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。给定先前成功的规划，将轨迹生成模型作为新规划问题的先验是非常理想的。先前的工作提出了几种利用该先验来引导运动规划问题的方法：要么对先验进行采样以进行初始化，要么在最大后验概率公式中使用先验分布进行轨迹优化。在这项工作中，我们提出学习扩散模型作为先验。随后，通过利用扩散模型的逆去噪过程，我们可以直接从以任务目标为条件的后验轨迹分布中进行采样。此外，最近研究表明，扩散能够有效编码高维环境中的数据多模态，这特别适合大型轨迹数据集。为了证明我们方法的有效性，我们在模拟的平面机器人和7自由度机械臂操作环境中，将我们提出的方法——Motion Planning Diffusion——与几个基线进行了比较。为了评估我们方法的泛化能力，我们在存在未见障碍物的环境中对其进行了测试。我们的实验表明，扩散模型是编码机器人运动高维轨迹分布的强大先验。
英文摘要：
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.
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。
