We present a learning-enhanced motion planner for differential drive mobile manipulators to improve efficiency, success rate, and optimality. For task representation encoder, we propose a keypoint sequence extraction module that maps boundary states to 3D space via differentiable forward kinematics. Point clouds and keypoints are encoded separately and fused with attention, enabling effective integration of environment and boundary states information. We also propose a primitive-based truncated diffusion model that samples from a biased distribution. Compared with vanilla diffusion model, this framework improves the efficiency and diversity of the solution. Denoised paths are refined by trajectory optimization to ensure dynamic feasibility and task-specific optimality. In cluttered 3D simulations, our method achieves higher success rate, improved trajectory diversity, and competitive runtime compared to vanilla diffusion and classical baselines. The source code is released at https://github.com/nmoma/nmoma .

翻译：我们提出了一种面向差动驱动移动机械臂的学习增强型运动规划器，旨在提升效率、成功率和最优性。在任务表示编码器方面，我们设计了一种关键点序列提取模块，通过可微正向运动学将边界状态映射至三维空间。点云与关键点分别编码后通过注意力机制融合，从而有效整合环境与边界状态信息。我们同时提出了一种基于基元的截断扩散模型，该模型从偏置分布中采样。相较于标准扩散模型，该框架提升了求解效率与多样性。去噪路径通过轨迹优化进行精炼，以确保动态可行性与任务特定最优性。在杂乱的三维仿真环境中，与标准扩散模型及传统基线方法相比，我们的方法实现了更高的成功率、更优的轨迹多样性以及具有竞争力的运行时间。源代码已发布于 https://github.com/nmoma/nmoma。