发表机构
Texas A&M University（德克萨斯农工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
NeurRAFT是基于锚点级流匹配与避障距离感知偏好调优的生成式机器人运动规划框架，可显著提升杂乱环境下的无碰撞规划性能，且能零样本迁移至Franka机器人。
AI 中文摘要
近期的端到端神经运动规划器可从原始传感器观测中生成轨迹，避免了经典规划器所需的特权几何模型，但在杂乱环境中实现无碰撞规划仍具挑战性。本文提出NeurRAFT，这是一个基于锚点级流匹配与避障距离感知偏好调优的生成式规划框架。与此前建模密集路径点序列、将容量用于冗余局部细节和平滑度的神经规划器不同，NeurRAFT基于紧凑的锚点路径点运行。我们使用雅可比加权损失训练规划器，该损失考虑每个锚点在任务空间中的影响。推理时，锚点通过两次积分步骤生成，随后通过三次样条插值恢复平滑的全分辨率轨迹。由于仅从正样本演示进行模仿学习无法区分无碰撞轨迹与近碰撞轨迹，测试时仍会出现易碰撞行为。我们不依赖事后修正，而是直接调整预训练规划器的分布以转向更安全的解决方案，且不增加推理开销。具体而言，直接偏好优化将概率质量转移至具有更大避障距离的轨迹，所产生的改进直接被规划器参数吸收。实验表明，NeurRAFT较现有最优规划器有显著提升，而真实世界实验验证其在带噪和部分遮挡的深度观测下，可零样本迁移至Franka机器人。视频结果可在该https URL获取。
英文摘要
Recent end-to-end neural motion planners generate trajectories from raw sensor observations, avoiding the privileged geometric models required by classical planners. However, collision-free planning in cluttered environments remains challenging. We present NeurRAFT, a generative planning framework based on anchor-level flow matching and clearance-aware preference tuning. Unlike prior neural planners that model dense waypoint sequences and spend capacity on redundant local details and smoothness, NeurRAFT operates on compact anchor waypoints. We train the planner using a Jacobian-weighted loss that accounts for the task-space impact of each anchor. At inference, the anchors are generated in two integration steps, followed by cubic-spline interpolation to recover a smooth, full-resolution trajectory. Since imitation learning from positive demonstrations cannot distinguish collision-free from near-collision trajectories, collision-prone behaviors persist at test time. Rather than relying on post-hoc corrections, we directly reshape the pretrained planner's distribution toward safer solutions without augmenting inference. Specifically, Direct Preference Optimization shifts probability mass toward trajectories with larger obstacle clearance, with the resulting improvement directly absorbed into the planner parameters. Experiments show substantial improvements over state-of-the-art planners, while real-world experiments demonstrate zero-shot transfer to a Franka robot under noisy and partially occluded depth observations. Video results available at https://neurraft.github.io/.
