用扩散模型解决冗余机械臂运动规划难题，提升避障能力。
RobotDiffuse: Diffusion-Based Motion Planning for Redundant Manipulators with the ROP Obstacle Avoidance Dataset
- 基于扩散模型与点云编码器，结合物理约束生成平滑轨迹。
- 在ROP数据集上实现3500万姿态、14万避障场景的高精度规划。
- 适合研究机器人运动规划与生成式建模的开发者参考。
冗余机械臂因自由度更高，具备更强的运动性能和灵活性，适用于制造、手术机器人及人机协作等场景。然而，其运动规划受高维空间与复杂动态环境影响，传统算法难以应对，深度学习方法则常面临不稳定与效率低的问题。本文提出RobotDiffuse，一种基于扩散模型的冗余机械臂运动规划方法。通过融合物理约束与点云编码器，并以仅编码器的Transformer替代U-Net结构，提升了模型对时序依赖的捕捉能力，生成更平滑、连贯的运动轨迹。我们在复杂仿真环境中验证该方法，并发布新数据集Robot-obtalcles-panda（ROP），包含3500万机器人姿态与14万避障场景。实验最高得分证明了RobotDiffuse的有效性，彰显扩散模型在运动规划中的潜力。数据集开源地址：https://github.com/ACRoboT-buaa/RobotDiffuse。
原文摘要 · Abstract (English)
Redundant manipulators, with their higher Degrees of Freedom (DoFs), offer enhanced kinematic performance and versatility, making them suitable for applications like manufacturing, surgical robotics, and human-robot collaboration. However, motion planning for these manipulators is challenging due to increased DoFs and complex, dynamic environments. While traditional motion planning algorithms struggle with high-dimensional spaces, deep learning-based methods often face instability and inefficiency in complex tasks. This paper introduces RobotDiffuse, a diffusion model-based approach for motion planning in redundant manipulators. By integrating physical constraints with a point cloud encoder and replacing the U-Net structure with an encoder-only transformer, RobotDiffuse improves the model's ability to capture temporal dependencies and generate smoother, more coherent motion plans. We validate the approach using a complex simulator and release a new dataset, Robot-obtalcles-panda (ROP), with 35M robot poses and 0.14M obstacle avoidance scenarios. The highest overall score obtained in the experiment demonstrates the effectiveness of RobotDiffuse and the promise of diffusion models for motion planning tasks. The dataset can be accessed at https://github.com/ACRoboT-buaa/RobotDiffuse.
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