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This paper introduces HiPAN, a hierarchical navigation framework for quadruped robots that directly processes onboard depth images to generate strategic navigation commands and posture adaptations. It addresses the limitations of traditional mapping-planning pipelines by learning a high-level policy for strategic navigation and a low-level posture-adaptive locomotion controller. Path-Guided Curriculum Learning is used to train the high-level policy, enabling long-horizon navigation and improved performance in complex 3D environments, demonstrated in both simulation and real-world experiments.
Quadruped robots can now nimbly navigate complex 3D terrain using only onboard depth images, thanks to a hierarchical policy that learns strategic navigation and posture adaptation.
Navigating quadruped robots in unstructured 3D environments poses significant challenges, requiring goal-directed motion, effective exploration to escape from local minima, and posture adaptation to traverse narrow, height-constrained spaces. Conventional approaches employ a sequential mapping-planning pipeline but suffer from accumulated perception errors and high computational overhead, restricting their applicability on resource-constrained platforms. To address these challenges, we propose Hierarchical Posture-Adaptive Navigation (HiPAN), a framework that operates directly on onboard depth images at deployment. HiPAN adopts a hierarchical design: a high-level policy generates strategic navigation commands (planar velocity and body posture), which are executed by a low-level, posture-adaptive locomotion controller. To mitigate myopic behaviors and facilitate long-horizon navigation, we introduce Path-Guided Curriculum Learning, which progressively extends the navigation horizon from reactive obstacle avoidance to strategic navigation. In simulation, HiPAN achieves higher navigation success rates and greater path efficiency than classical reactive planners and end-to-end baselines, while real-world experiments further validate its applicability across diverse, unstructured 3D environments.
