Running optimization across many parallel seeds leveraging GPU compute have relaxed the need for a good initialization, but this can fail if the problem is highly non-convex as all seeds could get stuck in local minima. One such setting is collision-free motion optimization for robot manipulation, where optimization converges quickly on easy problems but struggle in obstacle dense environments (e.g., a cluttered cabinet or table). In these situations, graph-based planning algorithms are used to obtain seeds, resulting in significant slowdowns. We propose DiffusionSeeder, a diffusion based approach that generates trajectories to seed motion optimization for rapid robot motion planning. DiffusionSeeder takes the initial depth image observation of the scene and generates high quality, multi-modal trajectories that are then fine-tuned with a few iterations of motion optimization. We integrate DiffusionSeeder to generate the seed trajectories for cuRobo, a GPU-accelerated motion optimization method, which results in 12x speed up on average, and 36x speed up for more complicated problems, while achieving 10% higher success rate in partially observed simulation environments. Our results show the effectiveness of using diverse solutions from a learned diffusion model. Physical experiments on a Franka robot demonstrate the sim2real transfer of DiffusionSeeder to the real robot, with an average success rate of 86% and planning time of 26ms, improving on cuRobo by 51% higher success rate while also being 2.5x faster.

翻译：利用GPU计算并行运行多个种子点的优化已降低了对良好初始化的需求，但当问题高度非凸时，该方法可能失效，因为所有种子点都可能陷入局部极小值。机器人操作中的无碰撞运动优化正是此类场景之一：在简单问题上优化能快速收敛，但在障碍物密集的环境（如杂乱的柜子或桌子）中则举步维艰。此类情况下通常需采用基于图的规划算法获取种子点，导致规划速度显著下降。我们提出DiffusionSeeder——一种基于扩散的方法，通过生成轨迹为机器人快速运动规划提供运动优化种子。DiffusionSeeder接收场景的初始深度图像观测，生成高质量、多模态的轨迹，再通过少量运动优化迭代进行微调。我们将DiffusionSeeder集成至GPU加速的运动优化方法cuRobo中，用于生成种子轨迹，实验表明：在部分可观测的仿真环境中，平均加速比达12倍，复杂问题加速比最高达36倍，且成功率提升10%。研究结果验证了从学习型扩散模型中获取多样化解的有效性。在Franka机器人上的物理实验证明了DiffusionSeeder向真实机器人的仿真到现实迁移能力，平均成功率达86%，规划时间仅26毫秒，较cuRobo成功率提升51%，同时规划速度加快2.5倍。