Efficient Navigation Among Movable Obstacles using a Mobile Manipulator via Hierarchical Policy Learning

-- 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. Robust robot navigation in complex environments is crucial for applications ranging from delivery [1] to warehouse automation [2].