用关键点与扩散模型结合，高效生成差速移动机械臂的运动轨迹。
Primitive-based Truncated Diffusion for Efficient Trajectory Generation of Differential Drive Mobile Manipulators
- 通过可微正运动学提取关键点，融合环境点云与边界状态信息。
- 基于动作原型的截断扩散模型提升路径采样效率与多样性。
- 适合需要高成功率和动态可行轨迹的机器人路径规划场景。
我们提出一种增强学习的运动规划方法，用于差速驱动移动机械臂，以提升效率、成功率与最优性。任务表示编码器设计关键点序列提取模块，利用可微正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，有效整合环境与边界状态信息。我们还提出基于动作原型的截断扩散模型，从偏置分布中采样，相比原始扩散模型显著提升解的效率与多样性。去噪路径经轨迹优化后确保动态可行性与任务特定最优性。在复杂三维仿真中，该方法相较原始扩散模型与经典基线，实现更高成功率、更好轨迹多样性及具有竞争力的运行时间。源代码已公开于 https://github.com/nmoma/nmoma。
原文摘要 · Abstract (English)
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 .
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