用分层强化学习让机器人在复杂人群里安全导航，无需高保真仿真。
Hierarchical Learning-Enhanced MPC for Safe Crowd Navigation with Heterogeneous Constraints
- 用图神经网络+强化学习估算代价，生成局部目标建议。
- 结合时空路径搜索与增量动作掩码，实现端到端可训练的规划器。
- 真实场景验证效果领先，计算效率高，适合实际部署。
本文提出一种新型分层框架，用于动态环境中具有异构约束的机器人导航。方法通过强化学习训练的图神经网络高效估计机器人的代价到目标（cost-to-go），并将其形式化为局部目标推荐。随后，引入考虑运动学约束的时空路径搜索模块，生成参考轨迹以辅助求解显式约束强制的非凸优化问题。更重要的是，我们设计了增量动作掩码机制和特权学习策略，实现了所提规划器的端到端训练。仿真与真实世界实验均表明，该方法在复杂动态环境中有效解决局部规划问题，达到当前最优（SOTA）性能。相比现有学习-优化混合方法，本方案无需依赖高保真仿真环境，在计算效率与训练可扩展性方面具有显著优势。代码将在论文被接收后开源。
原文摘要 · Abstract (English)
In this paper, we propose a novel hierarchical framework for robot navigation in dynamic environments with heterogeneous constraints. Our approach leverages a graph neural network trained via reinforcement learning (RL) to efficiently estimate the robot's cost-to-go, formulated as local goal recommendations. A spatio-temporal path-searching module, which accounts for kinematic constraints, is then employed to generate a reference trajectory to facilitate solving the non-convex optimization problem used for explicit constraint enforcement. More importantly, we introduce an incremental action-masking mechanism and a privileged learning strategy, enabling end-to-end training of the proposed planner. Both simulation and real-world experiments demonstrate that the proposed method effectively addresses local planning in complex dynamic environments, achieving state-of-the-art (SOTA) performance. Compared with existing learning-optimization hybrid methods, our approach eliminates the dependency on high-fidelity simulation environments, offering significant advantages in computational efficiency and training scalability. The code will be released as open-source upon acceptance of the paper.
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