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

翻译：我们提出了一种学习引导的运动规划框架，该框架利用扩散模型生成用于轨迹优化的种子轨迹。给定工作空间，我们的方法通过由一组稀疏的、与任务相关的关键构型组成的环境表示来近似构型空间障碍物，并将其作为扩散模型的条件输入。该扩散模型在训练过程中整合了鼓励平滑、无碰撞轨迹的正则化项，而轨迹优化则对生成的种子轨迹进行细化，以修正任何发生碰撞的片段。我们的实验结果表明，通过我们基于构型空间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，其性能优于以往基于学习和规划的基线方法。视频和补充材料可在项目页面找到：https://kiwi-sherbet.github.io/PRESTO。