Learning Terrain-Specialized Policies for Adaptive Locomotion in Challenging Environments

• URL: http://arxiv.org/abs/2509.20635v2
• Date: Mon, 03 Nov 2025 20:32:45 GMT
• Title: Learning Terrain-Specialized Policies for Adaptive Locomotion in Challenging Environments
• Abstract summary: This work introduces a hierarchical reinforcement learning framework that leverages terrain-specialized policies and curriculum learning to enhance agility and tracking performance in complex environments.<n>We validated our method on simulation, where our approach outperforms a generalist policy by up to 16% in success rate and achieves lower tracking errors as the velocity target increases.
• Score: 0.4588028371034407
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Legged robots must exhibit robust and agile locomotion across diverse, unstructured terrains, a challenge exacerbated under blind locomotion settings where terrain information is unavailable. This work introduces a hierarchical reinforcement learning framework that leverages terrain-specialized policies and curriculum learning to enhance agility and tracking performance in complex environments. We validated our method on simulation, where our approach outperforms a generalist policy by up to 16% in success rate and achieves lower tracking errors as the velocity target increases, particularly on low-friction and discontinuous terrains, demonstrating superior adaptability and robustness across mixed-terrain scenarios.

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