We introduce a learning-guided motion planning framework that provides initial seed trajectories using a diffusion model for trajectory optimization. Given a workspace, our method approximates the configuration space (C-space) obstacles through a key-configuration representation that consists of a sparse set of task-related key configurations, and uses this as an input to the diffusion model. The diffusion model integrates regularization terms that encourage collision avoidance and smooth trajectories during training, and trajectory optimization refines the generated seed trajectories to further correct any colliding segments. Our experimental results demonstrate that using high-quality trajectory priors, learned through our C-space-grounded diffusion model, enables efficient generation of collision-free trajectories in narrow-passage environments, outperforming prior learning- and planning-based baselines. Videos and additional materials can be found on the project page: https://kiwi-sherbet.github.io/PRESTO.

翻译：本文提出一种学习引导的运动规划框架，该框架利用扩散模型为轨迹优化提供初始种子轨迹。针对给定工作空间，本方法通过关键构型表示来近似构型空间（C-space）障碍物，该表示由一组稀疏的任务相关关键构型构成，并以此作为扩散模型的输入。扩散模型在训练过程中整合了鼓励避碰和平滑轨迹的正则化项，轨迹优化则对生成的种子轨迹进行细化以进一步修正任何碰撞段。实验结果表明，通过我们基于C-space构建的扩散模型学习到的高质量轨迹先验，能够在狭窄通道环境中高效生成无碰撞轨迹，其性能优于现有的基于学习与规划的基线方法。视频及补充材料详见项目页面：https://kiwi-sherbet.github.io/PRESTO。