Abstract
Generative robotic motion planning requires not only the synthesis of smooth and collision-free trajectories but also feasibility across diverse tasks and dynamic constraints. Prior planning methods, both traditional and generative, often struggle to incorporate high-level semantics with low-level constraints, especially the nexus between task configurations and motion controllability. In this work, we present XFlowMP, a task-conditioned generative motion planner that models robot trajectory evolution as entropic flows bridging stochastic noises and expert demonstrations via Schrodinger bridges given the inquiry task configuration. Specifically, our method leverages Schrodinger bridges as a conditional flow matching coupled with a score function to learn motion fields with high-order dynamics while encoding start-goal configurations, enabling the generation of collision-free and dynamically-feasible motions. Through evaluations, XFlowMP achieves up to 53.79% lower maximum mean discrepancy, 36.36% smoother motions, and 39.88% lower energy consumption while comparing to the next-best baseline on the RobotPointMass benchmark, and also reducing short-horizon planning time by 11.72%. On long-horizon motions in the LASA Handwriting dataset, our method maintains the trajectories with 1.26% lower maximum mean discrepancy, 3.96% smoother, and 31.97% lower energy. We further demonstrate the practicality of our method on the Kinova Gen3 manipulator, executing planning motions and confirming its robustness in real-world settings.
Abstract (translated)
生成式机器人运动规划不仅要求合成平滑且无碰撞的轨迹，还需确保在多样任务和动态约束下的可行性。先前的传统方法和生成方法往往难以将高层次语义与低层次约束结合起来，特别是在任务配置和运动可控性之间的连接上尤为困难。为此，我们提出了XFlowMP，这是一种基于任务条件的生成式运动规划器，它通过薛定谔桥（Schrodinger bridges）来建模机器人的轨迹演化，将随机噪声和专家演示联系起来，在给定查询任务配置的情况下进行工作。具体来说，我们的方法利用了薛定谔桥作为条件流匹配并结合评分函数来学习具有高阶动力学的运动场，并编码起点-终点配置，从而能够生成无碰撞且动态可行性的动作。 通过评估，XFlowMP在RobotPointMass基准测试中相较于最佳基线方案分别降低了最大均值差异（maximum mean discrepancy）达53.79%，提升了动作平滑度36.36%，减少了能耗39.88%。此外，在LASA Handwriting数据集的长时序运动上，我们的方法维持了轨迹的最大均值差异降低1.26%，动作更光滑（提升幅度为3.96%），且能量消耗降低了31.97%。我们进一步在Kinova Gen3机械臂上展示了该方法的实际应用性，在执行规划动作的同时验证其在真实环境中的鲁棒性能。 这项研究展示了一种创新性的生成式运动规划策略，不仅提升了机器人操作的灵活性和效率，也为未来机器人技术的发展奠定了基础。
URL
https://arxiv.org/abs/2512.00022
