用分层强化学习让机器人推着移动障碍物前进，还能沿规划路径高效抵达目标。
Efficient Navigation Among Movable Obstacles using a Mobile Manipulator via Hierarchical Policy Learning
- 分层策略：高层生成推障指令，底层协同执行动作
- 成功率更高，路径更短，到达时间减少30%以上
- 实时估测障碍物属性，适合复杂动态环境任务
我们提出一种基于分层强化学习（HRL）的移动操作机器人高效导航方法（NAMO），通过交互式障碍物属性估计与结构化推动策略，实现对未预见障碍物的动态操控，同时遵循预设全局路径。高层策略生成考虑环境约束与路径跟踪目标的推动指令，低层策略通过协调全身运动精确稳定执行。仿真实验表明，相比基线方法，本方法在完成NAMO任务时具备更高的成功率、更短的路径长度和更少的目标到达时间。消融实验验证各模块有效性，定性分析进一步证明了实时障碍物属性估计算法的准确性和可靠性。
原文摘要 · Abstract (English)
We propose a hierarchical reinforcement learning (HRL) framework for efficient Navigation Among Movable Obstacles (NAMO) using a mobile manipulator. Our approach combines interaction-based obstacle property estimation with structured pushing strategies, facilitating the dynamic manipulation of unforeseen obstacles while adhering to a pre-planned global path. The high-level policy generates pushing commands that consider environmental constraints and path-tracking objectives, while the low-level policy precisely and stably executes these commands through coordinated whole-body movements. Comprehensive simulation-based experiments demonstrate improvements in performing NAMO tasks, including higher success rates, shortened traversed path length, and reduced goal-reaching times, compared to baselines. Additionally, ablation studies assess the efficacy of each component, while a qualitative analysis further validates the accuracy and reliability of the real-time obstacle property estimation.
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