Scaling Rough Terrain Locomotion with Automatic Curriculum Reinforcement Learning

• URL: http://arxiv.org/abs/2601.17428v1
• Date: Sat, 24 Jan 2026 11:54:16 GMT
• Title: Scaling Rough Terrain Locomotion with Automatic Curriculum Reinforcement Learning
• Abstract summary: We propose a Learning Progress-based Automatic Curriculum Reinforcement Learning framework.<n>It estimates the agent's learning progress online and adaptively adjusts the task-sampling distribution.<n>It enables automatic curriculum generation without prior knowledge of the difficulty distribution over the task space.
• Score: 16.89722808439415
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Curriculum learning has demonstrated substantial effectiveness in robot learning. However, it still faces limitations when scaling to complex, wide-ranging task spaces. Such task spaces often lack a well-defined difficulty structure, making the difficulty ordering required by previous methods challenging to define. We propose a Learning Progress-based Automatic Curriculum Reinforcement Learning (LP-ACRL) framework, which estimates the agent's learning progress online and adaptively adjusts the task-sampling distribution, thereby enabling automatic curriculum generation without prior knowledge of the difficulty distribution over the task space. Policies trained with LP-ACRL enable the ANYmal D quadruped to achieve and maintain stable, high-speed locomotion at 2.5 m/s linear velocity and 3.0 rad/s angular velocity across diverse terrains, including stairs, slopes, gravel, and low-friction flat surfaces--whereas previous methods have generally been limited to high speeds on flat terrain or low speeds on complex terrain. Experimental results demonstrate that LP-ACRL exhibits strong scalability and real-world applicability, providing a robust baseline for future research on curriculum generation in complex, wide-ranging robotic learning task spaces.

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