GACL: 接地型適応カリキュラム学習と能動的タスク・性能モニタリング
GACL: Grounded Adaptive Curriculum Learning with Active Task and Performance Monitoring
ロボットタスク向けに、複雑なタスク空間を扱いながら目標分布への関連性を保つ適応的カリキュラム学習フレームワークを提案し、車輪ナビと四足歩行で成功率を向上させた。
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著者: Linji Wang, Zifan Xu, Peter Stone, Xuesu Xiao
分類: cs.RO, cs.AI
原文アブストラクト
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.
関連論文
- 深層強化学習のためのカリキュラム学習における教師アルゴリズムカリキュラム学習
