用扩散模型生成快速避障路径，关键配置表征环境提升效率
PRESTO: Fast Motion Planning Using Diffusion Models Based on Key-Configuration Environment Representation
- 通过关键配置稀疏表征环境，引导扩散模型生成初始路径
- 在狭窄通道场景下，生成路径成功率显著高于传统方法
- 适合需要快速生成高质量避障轨迹的机器人应用
我们提出一种学习引导的运动规划框架，利用扩散模型生成用于轨迹优化的种子路径。给定工作空间，该方法通过一组与任务相关的稀疏关键配置来近似构型空间（C-space）障碍物，并将其作为扩散模型的条件输入。扩散模型在训练中引入正则化项，鼓励生成平滑且无碰撞的轨迹；随后的轨迹优化阶段对生成的种子路径进行精修，修正任何发生碰撞的段落。实验结果表明，基于构型空间的扩散模型所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路径，优于先前的基于学习和基于规划的基线方法。视频与补充材料见项目主页：https://kiwi-sherbet.github.io/PRESTO。
原文摘要 · Abstract (English)
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.
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