通过自动课程强化学习扩展粗糙地形运动
Scaling Rough Terrain Locomotion with Automatic Curriculum Reinforcement Learning
AI总结：
本文提出基于学习进度的自动课程强化学习框架，使四足机器人在复杂地形上实现稳定高速运动，突破传统方法在地形适应上的限制。
摘要：
课程学习在机器人学习中已显示出显著的有效性。然而，当扩展到复杂、广泛的任务空间时，它仍然面临局限性。此类任务空间往往缺乏明确的难度结构，使先前方法所需的难度排序难以定义。我们提出了一种基于学习进度的自动课程强化学习（LP-ACRL）框架，该框架在线估计智能体的学习进度，并自适应地调整任务采样分布，从而实现无需事先了解任务空间难度分布的自动课程生成。使用LP-ACRL训练的策略使ANYmal D四足机器人能够在多种地形上实现并维持稳定的高速运动，包括台阶、斜坡、砾石和低摩擦平坦表面，而先前方法通常仅限于在平坦地形上高速运动或在复杂地形上低速运动。实验结果表明，LP-ACRL表现出强大的可扩展性和实际应用性，为未来在复杂、广泛机器人学习任务空间中进行课程生成的研究提供了稳健的基准。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
