The problem of path planning has been studied for years. Classic planning pipelines, including perception, mapping, and path searching, can result in latency and compounding errors between modules. While recent studies have demonstrated the effectiveness of end-to-end learning methods in achieving high planning efficiency, these methods often struggle to match the generalization abilities of classic approaches in handling different environments. Moreover, end-to-end training of policies often requires a large number of labeled data or training iterations to reach convergence. In this paper, we present a novel Imperative Learning (IL) approach. This approach leverages a differentiable cost map to provide implicit supervision during policy training, eliminating the need for demonstrations or labeled trajectories. Furthermore, the policy training adopts a Bi-Level Optimization (BLO) process, which combines network update and metric-based trajectory optimization, to generate a smooth and collision-free path toward the goal based on a single depth measurement. The proposed method allows task-level costs of predicted trajectories to be backpropagated through all components to update the network through direct gradient descent. In our experiments, the method demonstrates around 4x faster planning than the classic approach and robustness against localization noise. Additionally, the IL approach enables the planner to generalize to various unseen environments, resulting in an overall 26-87% improvement in SPL performance compared to baseline learning methods.

翻译：路径规划问题已被研究多年。经典规划流程（包括感知、建图与路径搜索）会导致模块间的延迟与累积误差。尽管近期研究证明了端到端学习方法在实现高规划效率方面的有效性，但这些方法往往难以达到经典方法在不同环境下的泛化能力。此外，策略的端到端训练通常需要大量标注数据或多次训练迭代才能收敛。本文提出一种新型的指令式学习（IL）方法。该方法利用可微分代价映射在策略训练过程中提供隐式监督，无需演示或标注轨迹。同时，策略训练采用双层优化（BLO）过程，结合网络更新与基于度量的轨迹优化，基于单次深度测量生成平滑且无碰撞的通往目标路径。所提方法允许预测轨迹的任务级代价通过所有组件反向传播，以直接梯度下降方式更新网络。实验表明，该方法规划速度约为经典方法的4倍，且对定位噪声具有鲁棒性。此外，指令式学习方法使规划器能泛化至多种未见环境，相比基线学习方法，其SPL性能整体提升26-87%。