Learning Terrain-Specialized Policies for Adaptive Locomotion in Challenging Environments

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.

Accepted to the 22nd International Conference on Advanced Robotics (ICAR 2025). 7 pages

References in corpus (5)

• Learning agile and dynamic motor skills for legged robots
• Learning Quadrupedal Locomotion over Challenging Terrain
• Learning robust perceptive locomotion for quadrupedal robots in the wild
• Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments
• Lifelike Agility and Play in Quadrupedal Robots using Reinforcement Learning and Generative Pre-trained Models