用分层扩散模型让机器人在复杂环境中无碰撞规划路径
Cascaded Diffusion Models for Neural Motion Planning
- 分层结构结合全局预测与局部优化，动态修复轨迹
- 在导航和操作任务中比基线方法提升约5%成功率
- 适合需要高精度避障的机器人路径规划场景
真实世界中的机器人需在复杂环境里感知并移动至目标点，同时避免碰撞，尤其在传感器感知受限且目标位于杂乱区域时更具挑战。尽管扩散策略等生成模型在解决局部规划问题上表现优异，但在处理真正复杂的全局运动规划时，仍难以避免细微的约束违反。本文提出一种基于扩散策略的全局运动规划方法，使机器人能在复杂场景中生成完整无碰撞的轨迹，并综合考虑路径上的多重障碍物。该方法采用级联式分层模型，融合全局预测与局部修正，并引入在线规划修复机制，确保轨迹无碰撞。在多个领域（包括导航与操作）的挑战性任务中，该方法性能优于多种基线模型，平均提升约5%。
原文摘要 · Abstract (English)
Robots in the real world need to perceive and move to goals in complex environments without collisions. Avoiding collisions is especially difficult when relying on sensor perception and when goals are among clutter. Diffusion policies and other generative models have shown strong performance in solving local planning problems, but often struggle at avoiding all of the subtle constraint violations that characterize truly challenging global motion planning problems. In this work, we propose an approach for learning global motion planning using diffusion policies, allowing the robot to generate full trajectories through complex scenes and reasoning about multiple obstacles along the path. Our approach uses cascaded hierarchical models which unify global prediction and local refinement together with online plan repair to ensure the trajectories are collision free. Our method outperforms (by ~5%) a wide variety of baselines on challenging tasks in multiple domains including navigation and manipulation.
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