发表机构
School of Automation and Intelligent Sensing, Shanghai Jiao Tong University（上海交通大学自动化与智能感知学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对机器人运动规划中刚体变换等变性难题，提出BridgeFlow框架，通过轻量级模块实现等变性，结合布朗桥信息先验与最优传输加速推理，并嵌入环境感知，相比基线有速度和轨迹率优势，可稳健泛化。
AI 中文摘要
在机器人运动规划中，刚体变换的等变性对稳健的空间泛化至关重要。当前基于学习的规划器面临两难：要么缺乏内在等变性，将变换后的任务视为新场景；要么通过计算昂贵的专门架构来实现，限制实时推理。为打破这种权衡，我们提出BridgeFlow，一个快速且严格SE(2)等变的生成式运动规划框架。它通过轻量级以任务为中心的规范化模块实现精确空间等变性，使用标准架构进行泛化。为进一步加速推理，将布朗桥信息先验与上下文感知小批量最优传输配对。通过无分类器引导明确嵌入环境感知。在密集2D环境和7自由度Franka机械手的评估表明，BridgeFlow比现有扩散基线推理速度快15倍，有效轨迹率高2倍，能稳健泛化到全新环境和任意空间变换。
英文摘要
In robotic motion planning, equivariance to rigid body transformations is crucial for robust spatial generalization. However, current learning-based planners face a critical dilemma: they either lack inherent equivariance, treating transformed tasks as novel scenarios, or enforce it via computationally expensive specialized architectures that bottleneck real-time inference. To break this trade-off, we propose BridgeFlow, a fast and strictly SE(2)-equivariant generative motion planning framework. Rather than relying on heavy equivariant networks, BridgeFlow achieves exact spatial equivariance via a lightweight task-centric canonicalization module, enabling generalization using standard architectures. To further accelerate inference, we pair a Brownian bridge informative prior with context-aware mini-batch optimal transport. This constructs a straightened vector field that minimizes transport costs and stabilizes training. Furthermore, environmental awareness is explicitly embedded via Classifier-Free Guidance. Evaluations in dense 2D environments and on a 7-DoF Franka manipulator demonstrate that BridgeFlow achieves up to a 15x inference speedup and a 2x higher valid trajectory rate over state-of-the-art diffusion baselines, alongside robust generalization to entirely unseen environments and arbitrary spatial transformations.
CommentsAccepted to the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026). 8 pages, 7 figures
