Tune Slowly, Control Quickly: Learning a Better Robot Navigation Stack

Autonomous navigation in complex, unstructured environments poses a significant challenge, with traditional planners lacking adaptability and end-to-end learning methods hindered by data dependency or training instability. This paper proposes a hierarchical learning-based hybrid method with sim-to-real transfer.