GACL: Grounded Adaptive Curriculum Learning with Active Task and Performance Monitoring
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 30 of its 35 references that are in the map. First posted on arXiv 5 Aug 2025.
Builds on
30 of 35 references.
References · 30
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18 more
- 2021APPL: Adaptive Planner Parameter Learningcited by 20
- 2022CLUTR: Curriculum Learning via Unsupervised Task Representation Learningcited by 20
- 2020APPLI: Adaptive Planner Parameter Learning From Interventionscited by 19
- 2022Benchmarking Reinforcement Learning Techniques for Autonomous Navigationcited by 19
- 2024Curriculum-Based Reinforcement Learning for Quadrupedal Jumping: A Reference-free Designcited by 18
- 2020APPLR: Adaptive Planner Parameter Learning from Reinforcementcited by 16
- 2021APPLE: Adaptive Planner Parameter Learning from Evaluative Feedbackcited by 16
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- 2023Learning to Model and Plan for Wheeled Mobility on Vertically Challenging Terraincited by 11
- 2024Dexterous Legged Locomotion in Confined 3D Spaces with Reinforcement Learningcited by 9
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- 2024Reinforcement Learning for Wheeled Mobility on Vertically Challenging Terraincited by 7
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- 2023Team Coordination on Graphs with State-Dependent Edge Costcited by 4
- 2023Automatic Curriculum Learning with Gradient Reward Signalscited by 3
- 2024Scaling Team Coordination on Graphs with Reinforcement Learningcited by 3
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Citing papers per year: 2026: 3.
3 papers.