aiqbench analysis
This article focuses on SEA-Nav, a reinforcement learning framework designed to enable safe and agile navigation for quadruped robots in cluttered environments. By integrating differentiable control barrier functions with adaptive collision-state initialization, the method achieves zero-shot transfer to real-world scenarios after only one hour of training on a single GPU. The core contribution lies in advancing machine learning techniques for robotics safety and efficiency rather than addressing general consumer or infrastructure topics.
SEA-Nav: Efficient Policy Learning for Safe and Agile Quadruped Navigation in Cluttered Environments
arXiv:2603.09460v2 Announce Type: replace 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.
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.
Robotics (cs.RO)
arXiv:2603.09460 [cs.RO]
(or arXiv:2603.09460v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2603.09460 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 202 words
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