LAGO策略：面向平滑操作的延迟感知异步扩散策略与目标导向无碰撞规划
LAGO Policy: Latency-Aware Asynchronous Diffusion Policies with Goal-Directed Collision-Free Planning for Smooth Manipulation
AI总结：
提出LAGO策略，通过延迟感知条件引导和时空轨迹优化，解决异步扩散策略的间断和碰撞问题，实现平滑安全的操作。
摘要：
基于扩散的视觉运动策略在异步推理部署时，常出现片段间不连续，且缺乏显式的障碍物感知机制，导致运动抖动和碰撞，阻碍了在真实场景中的可靠操作。为解决这些问题，我们提出LAGO策略，一个统一的异步动作生成框架，将轨迹优化与扩散策略相结合，实现平滑安全的执行。LAGO策略通过基于未来动作的延迟感知无分类器引导条件，提高了片段间一致性。它进一步通过从演示中预测任务相关的交互目标，实现目标导向的无碰撞轨迹规划。最后，时空轨迹优化细化待执行的动作，以实现低抖动和可行的运动。大量真实世界实验表明，LAGO策略在具有挑战性的操作任务中，实现了平滑无碰撞的执行和高任务成功率。项目网站：此 https URL
英文摘要：
Diffusion-based visuomotor policies deployed with asynchronous inference often exhibit inter-chunk discontinuities and lack explicit mechanisms for obstacle-aware execution, leading to jerky motions and collisions that hinder reliable manipulation in real-world scenes. To address these issues, we propose LAGO Policy, a unified asynchronous action-generation framework that integrates trajectory optimization with diffusion policy for smooth and safe execution. LAGO Policy improves inter-chunk consistency via latency-aware classifier-free guidance conditioning on future actions. It further enables goal-directed collision-free trajectory planning by predicting a task-relevant interaction goal from demonstrations. Finally, spatial-temporal trajectory optimization refines the actions to be executed for low-jerk and feasible motion. Extensive real-world experiments demonstrate that LAGO Policy achieves smooth collision-free execution with high task success across challenging manipulation tasks. Project Website: https://lago-policy.github.io/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
