用扩散模型去噪生成多机器人无碰撞轨迹，速度快于传统方法。
D4orm: Multi-Robot Trajectories with Dynamics-aware Diffusion Denoised Deformations
- 通过迭代去噪噪声轨迹生成可行路径，无需训练
- 16个机器人2D场景下速度接近MPPI两倍
- 支持不同动力学模型，可实时优化，适合飞行器部署
本文提出一种优化方法，用于生成满足运动学与动力学约束且无碰撞的多机器人轨迹。核心思想是仅通过从分布中采样噪声轨迹并进行去噪，即可发现高质量轨迹。该方法无学习成分，仅依赖两个要素：机器人动力学模型以通过滚动模拟获得可行轨迹，以及基于蒙特卡洛梯度近似的适应度函数引导去噪过程。所提框架通过当前去噪过程对前一轨迹进行变形优化，支持随时增量修正，适用于不同动力学系统，并可借助GPU加速。在差速驱动与全向机器人团队中，2D与3D环境中最多支持16个机器人，实验表明其发现高质量解的速度优于其他黑箱优化方法（如MPPI）。在16个机器人的2D全向场景中，效率接近MPPI的两倍。作为可行性验证，我们成功实现8架多旋翼无人机的零样本轨迹部署。代码与视频见：https://github.com/proroklab/d4orm
原文摘要 · Abstract (English)
This work presents an optimization method for generating kinodynamically feasible and collision-free multi-robot trajectories that exploits an incremental denoising scheme in diffusion models. Our key insight is that high-quality trajectories can be discovered merely by denoising noisy trajectories sampled from a distribution. This approach has no learning component, relying instead on only two ingredients: a dynamical model of the robots to obtain feasible trajectories via rollout, and a fitness function to guide denoising with Monte Carlo gradient approximation. The proposed framework iteratively optimizes a deformation for the previous trajectory with the current denoising process, allows anytime refinement as time permits, supports different dynamics, and benefits from GPU acceleration. Our evaluations for differential-drive and holonomic teams with up to 16 robots in 2D and 3D worlds show its ability to discover high-quality solutions faster than other black-box optimization methods such as MPPI. In a 2D holonomic case with 16 robots, it is almost twice as fast. As evidence for feasibility, we demonstrate zero-shot deployment of the planned trajectories on eight multirotors. Code and video: https://github.com/proroklab/d4orm
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