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.

翻译：传统的基于优化的规划器虽然有效，但计算成本高，导致轨迹生成缓慢。一种降低计算时间的成功策略是使用模仿学习从这些规划器中开发出快速的神经网络策略，这些规划器被视为专家演示者。尽管由此产生的神经网络策略能有效快速生成与专家轨迹相似的轨迹，但其输出（1）未明确考虑动态可行性，且（2）策略无法适应训练时所用约束条件之外的变化。为克服这些局限，我们提出约束引导扩散（CGD），一种基于模仿学习的轨迹规划新方法。CGD采用混合学习/在线优化方案，将扩散策略与替代性高效优化问题相结合，从而能够生成无碰撞、动态可行的轨迹。CGD的核心思路是将专家求解的原始挑战性优化问题划分为两个更易处理的子问题：（a）高效寻找无碰撞路径，以及（b）为这些路径确定动态可行的时域参数化以获得轨迹。与传统神经网络架构相比，通过数值评估，我们在训练过程中从未遇到的新约束场景下，观察到性能和动态可行性的显著提升。