发表机构
Delft University of Technology（代尔夫特理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出混合流匹配规划器，从少量真实演示中学习任务与运动规划，利用数据增广和闭环重规划，在仿真和真实移动机械臂上验证了有效性。
AI 中文摘要
长时程移动操作需要任务规划以及执行该规划的运动。基于演示训练的生成式规划器一次性生成两者，既不需要符号域也不需要搜索。然而，迄今为止，它们依赖于数千条固定基座机械臂的脚本化演示，并采用开环执行。本文提出了一种混合流匹配规划器：一个单一网络通过掩码离散流匹配生成符号规划，并通过连续流匹配生成运动轨迹。与之前的生成式规划器不同，我们旨在从更小规模的演示集中学习，并以闭环方式执行规划。数据的两个特性弥补了数据集规模小的不足。从任意中间动作恢复的演示本身就是一个演示，这增加了训练样本的数量，并使得在每个动作之后能够进行重新规划。同类对象是可互换的，这将一个目标的演示转化为每个排列目标的演示。我们的基础实现在68%的保留场景中生成有效规划；针对离散规划的训练和生成方案将其提高到76%，而在运动学仿真中，每个动作后的重新规划将任务完成率从40%提高到53%。该规划器在任务完成率上与仅运动的流匹配策略相当，同时额外提供符号规划，并且在任务完成率和规划有效性方面均优于先前的混合扩散公式。我们在真实的移动机械臂上验证了该规划器。视频和项目页面：此https URL
英文摘要
Long-horizon mobile manipulation requires a task plan and the motion that executes it. Generative planners trained on demonstration produce both in one pass, requiring neither a symbolic domain nor search. To date, however, they have relied on thousands of scripted demonstrations of fixed-base arms and executed open loop. This paper presents a hybrid flow matching planner: a single network generates the symbolic plan with masked discrete flow matching and the motion trajectory with continuous flow matching. Unlike prior generative planners, we aim to learn from a much smaller set of demonstrations and to execute the plan in closed loop. Two properties of the data compensate for the small dataset. A demonstration resumed from any of its intermediate actions is itself a demonstration, which multiplies the training samples and enables replanning after every action. Objects of the same kind are interchangeable, which turns demonstrations of one goal into demonstrations of every permuted goal. Our base implementation produces valid plans on 68% of held-out scenes; a training and generation scheme for the discrete plan raises this to 76%, and replanning after every action raises the task completion rate from 40% to 53% in a kinematic simulation. The planner matches the task completion rate of motion-only flow matching policies while additionally providing the symbolic plan, and it outperforms previous hybrid diffusion formulations on both task completion and plan validity. We validate the planner on a real mobile manipulator. Videos and project page: https://andreumatoses.github.io/research/hybrid-flow-planning
Comments9 pages, 9 figures, 1 table. Submitted to IEEE ICRA 2027. Project page: https://andreumatoses.github.io/research/hybrid-flow-planning
