DUGM-R：面向学习型局部导航的不确定性感知动态栅格地图与风险触发恢复
DUGM-R: Uncertainty-Aware Dynamic Grid Mapping and Risk-Triggered Recovery for Learned Local Navigation
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中文总结 AI 辅助
针对拥挤室内环境中学习型局部导航对动态障碍物表示敏感及训练后碰撞倾向问题，提出结合不确定性感知动态栅格地图与风险触发恢复的强化学习框架，在仿真和实体机器人上均提升了导航性能。
中文摘要 AI 辅助
在拥挤的室内环境中，学习型局部导航对动态障碍物运动的表示方式十分敏感，而名义策略训练完成后，碰撞倾向行为可能仍然存在。我们提出了一种风险感知的强化学习框架，通过不确定性感知的动态不确定性栅格地图（DUGM）和模块化的训练后恢复机制来解决这两个问题。DUGM在机器人中心表示中结合了局部占用、估计的障碍物运动以及运动估计不确定性。在名义策略被冻结后，从名义轨迹中训练一个有限时域风险价值函数（RVF），并用于在预测到继续执行名义策略将发生碰撞时触发专门的恢复策略。在留出的NVIDIA Isaac Sim临床物流基准上的实验表明，与静态和确定性替代方案相比，不确定性感知的动态表示改善了名义导航性能，而恢复机制进一步减轻了残余的碰撞倾向行为。完整的框架还直接部署在TurtleBot3上，无需策略微调、重新训练或针对特定场景的适配，保持了仿真中观察到的性能趋势。这些结果表明，不确定性感知的动态表示和训练后恢复为改进学习型局部导航提供了互补机制。
英文摘要
Learned local navigation in crowded indoor environments is sensitive to how dynamic obstacle motion is represented, while collision-prone behaviour may persist after nominal policy training. We present a risk-aware reinforcement-learning framework that addresses these two issues through an uncertainty-aware Dynamic Uncertainty Grid Map (DUGM) and a modular post-training recovery mechanism. DUGM combines local occupancy, estimated obstacle motion, and motion-estimation uncertainty in a robot-centric representation. After the nominal policy is frozen, a finite-horizon Risk Value Function (RVF) is trained from nominal rollouts and used to trigger a dedicated recovery policy when continued nominal execution is predicted to be collision-prone. Experiments in a held-out NVIDIA Isaac Sim clinical-logistics benchmark show that uncertainty-aware dynamic representation improves nominal navigation over static and deterministic alternatives, while the recovery mechanism further mitigates residual collision-prone behaviour. The complete framework is also deployed directly on a TurtleBot3 without policy fine-tuning, retraining, or site-specific adaptation, retaining the performance trend observed in simulation. These results indicate that uncertainty-aware dynamic representation and post-training recovery provide complementary mechanisms for improving learned local navigation.
发表机构
- Imperial College London（伦敦帝国理工学院）
机构由 AI 辅助整理，请以论文原文为准。
