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arXiv · 2603.09460

SEA-Nav: Efficient Policy Learning for Safe and Agile Quadruped Navigation in Cluttered Environments

Shiyi Chen·Mingye Yang·Haiyi Liu·Haiyan Mao·Jiaqi Zhang·Shuheng He·Debing Zhang·Zihao Qiu·Chun Zhang
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Abstract

Efficiently learning safe and agile quadruped navigation in densely cluttered environments remains difficult: existing methods often lack safety and agility, or become conservative in complex scenes and require long training schedules. We propose SEA-Nav (Safe, Efficient, and Agile Navigation), a safe reinforcement learning framework for quadruped navigation in cluttered environments. A differentiable control barrier function (CBF) shield constrains the policy to produce safe velocity commands. An adaptive collision-state initialization mechanism increases the probability of learning from safety-critical near-collision experience. An action regularization term further suppresses infeasible commands for physical deployment. The policy converges after about one hour of training on a single RTX 4090 and transfers zero-shot to real-world cluttered scenes.

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Shiyi Chen, Mingye Yang, Haiyi Liu, Haiyan Mao, Jiaqi Zhang, Shuheng He, Debing Zhang, Zihao Qiu, Chun Zhang. 2026-09-13. SEA-Nav: Efficient Policy Learning for Safe and Agile Quadruped Navigation in Cluttered Environments. https://arxiv.org/abs/2603.09460

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