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.

学习轨迹分布的先验信息可以帮助加速机器人运动规划优化，本工作提出了学习扩散模型作为先验信息的方法，并通过扩散模型的逆去噪过程在任务目标条件下直接从后验轨迹分布中进行采样，实验证明扩散模型是编码高维机器人运动轨迹分布的强先验。