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.

使用约束引导扩散（CGD）的混合学习 / 在线优化方案，将传统以优化为基础的规划器与模仿学习相结合，生成无碰撞、动态可行的轨迹，以解决计算成本高、动态可行性不明确和适应新约束的问题。