Abstract
Traditional optimization-based planners, while effective, suffer from high computational costs, resulting in slow trajectory generation. A successful strategy to reduce computation time involves using Imitation Learning (IL) to develop fast neural network (NN) policies from those planners, which are treated as expert demonstrators. Although the resulting NN policies are effective at quickly generating trajectories similar to those from the expert, (1) their output does not explicitly account for dynamic feasibility, and (2) the policies do not accommodate changes in the constraints different from those used during training. To overcome these limitations, we propose Constraint-Guided Diffusion (CGD), a novel IL-based approach to trajectory planning. CGD leverages a hybrid learning/online optimization scheme that combines diffusion policies with a surrogate efficient optimization problem, enabling the generation of collision-free, dynamically feasible trajectories. The key ideas of CGD include dividing the original challenging optimization problem solved by the expert into two more manageable sub-problems: (a) efficiently finding collision-free paths, and (b) determining a dynamically-feasible time-parametrization for those paths to obtain a trajectory. Compared to conventional neural network architectures, we demonstrate through numerical evaluations significant improvements in performance and dynamic feasibility under scenarios with new constraints never encountered during training.
Abstract (translated)
传统的优化规划器虽然在效果上很有效，但计算成本很高，导致轨迹生成速度较慢。成功减少计算时间的方法之一是使用模仿学习（IL）从这些规划器中开发快速神经网络（NN）策略，将它们视为专家演示者。尽管生成的NN策略在快速生成类似于专家轨迹方面非常有效，但（1）它们的输出没有明确考虑到动态可行性，（2）这些策略没有考虑到训练过程中约束的变化。为了克服这些限制，我们提出了约束引导扩散（CGD），一种新型的IL-基轨迹规划方法。CGD利用了一种结合扩散策略和代理高效优化问题的混合学习/在线优化方案，使得可以生成无碰撞、动态可行轨迹。CGD的关键思想包括将专家通过 IL 解决的原始具有挑战性的优化问题划分为两个更容易管理子问题：（a）高效地找到无碰撞路径，（b）为这些路径确定一个动态可行的时间参数，以获得轨迹。与传统的神经网络架构相比，我们通过数值评估展示了在训练过程中从未遇到过的新的约束条件下，性能和动态可行性都有显著的提高。
URL
https://arxiv.org/abs/2405.01758
