用扩散算法加速双臂机器人路径规划，速度提升35倍且误差更小。
Diffusion-Based Optimization for Accelerated Convergence of Redundant Dual-Arm Minimum Time Problems
- 用概率采样替代梯度计算，解决非凸优化难题。
- 运行时间减少35倍，笛卡尔误差降低34%。
- 适合需要高速高精度双臂协同的工业场景。
我们提出一种基于新型模型驱动扩散算法的框架，用于最小化冗余双臂机器人在跟踪特定相对笛卡尔路径时所需的时间。先前工作采用双层优化方法，推导出低层凸子问题的解析解，并用原始-对偶法求解高层非凸问题。然而，其基于梯度的方法导致计算开销大，且因梯度稀疏性无法直接施加沿关节轨迹的 $L_{ ty}$ 笛卡尔误差约束。本文提出的扩散框架通过概率采样解决上述挑战，在高层非凸问题中实现35倍的运行时间降低和相比之前工作34%的更小笛卡尔误差。
原文摘要 · Abstract (English)
We present a framework leveraging a novel variant of the model-based diffusion algorithm to minimize the time required for a redundant dual-arm robot configuration to follow a desired relative Cartesian path. Our prior work proposed a bi-level optimization approach for the dual-arm problem, where we derived the analytical solution to the lower-level convex sub-problem and solved the high-level nonconvex problem using a primal-dual approach. However, the gradient-based nature leads to a large computation overhead, and it prohibits directly imposing an $L_{\infty}$ Cartesian error constraint along the joint trajectory due to the sparsity of the gradient. In this work, we propose a diffusion-based framework that relies on probabilistic sampling to tackle the aforementioned challenges in the nonconvex high-level problem, leading to a 35x reduction in the runtime and 34\% less Cartesian error compared to our prior work.
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