Title: GACL: Grounded Adaptive Curriculum Learning with Active Task and Performance Monitoring Show Chinese title

Title: GACL：基于主动任务和性能监控的接地自适应课程学习

Abstract: Curriculum learning has emerged as a promising approach for training complex robotics tasks, yet current applications predominantly rely on manually designed curricula, which demand significant engineering effort and can suffer from subjective and suboptimal human design choices. While automated curriculum learning has shown success in simple domains like grid worlds and games where task distributions can be easily specified, robotics tasks present unique challenges: they require handling complex task spaces while maintaining relevance to target domain distributions that are only partially known through limited samples. To this end, we propose Grounded Adaptive Curriculum Learning, a framework specifically designed for robotics curriculum learning with three key innovations: (1) a task representation that consistently handles complex robot task design, (2) an active performance tracking mechanism that allows adaptive curriculum generation appropriate for the robot's current capabilities, and (3) a grounding approach that maintains target domain relevance through alternating sampling between reference and synthetic tasks. We validate GACL on wheeled navigation in constrained environments and quadruped locomotion in challenging 3D confined spaces, achieving 6.8% and 6.1% higher success rates, respectively, than state-of-the-art methods in each domain. Show Chinese abstract

Abstract: 课程学习已成为训练复杂机器人任务的一种有前景的方法，但当前的应用主要依赖于手动设计的课程，这需要大量的工程努力，并且可能受到主观和次优的人为设计选择的影响。 尽管自动化课程学习在简单领域如网格世界和游戏中取得了成功，这些领域的任务分布可以轻松指定，但机器人任务带来了独特的挑战：它们需要处理复杂的任务空间，同时保持对目标领域分布的相关性，而这些分布仅通过有限样本部分已知。 为此，我们提出了基于基础的自适应课程学习，这是一个专门针对机器人课程学习的框架，具有三个关键创新：(1) 一种能够一致处理复杂机器人任务设计的任务表示，(2) 一种主动性能跟踪机制，允许根据机器人的当前能力生成自适应课程，(3) 一种通过参考任务和合成任务交替采样来保持目标领域相关性的基础方法。 我们在受限环境中的轮式导航和挑战性三维封闭空间中的四足运动上验证了GACL，分别比每个领域的最先进方法提高了6.8%和6.1%的成功率。