# GACL: Grounded Adaptive Curriculum Learning with Active Task and Performance Monitoring

arXiv: https://arxiv.org/abs/2508.02988 · HTML: https://mlsanity.com/paper/2508.02988/

## 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 (most cited first, 30 of 30)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation](https://mlsanity.com/paper/1806.10293/) (2018)
- [Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey](https://mlsanity.com/paper/2003.04960/) (2020)
- [Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design](https://mlsanity.com/paper/2012.02096/) (2020)
- [Automatic Curriculum Learning For Deep RL: A Short Survey](https://mlsanity.com/paper/2003.04664/) (2020)
- [Composable Deep Reinforcement Learning for Robotic Manipulation](https://mlsanity.com/paper/1803.06773/) (2018)
- [Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation](https://mlsanity.com/paper/2209.10780/) (2022)
- [Learning Inverse Kinodynamics for Accurate High-Speed Off-Road Navigation on Unstructured Terrain](https://mlsanity.com/paper/2102.12667/) (2021)
- [VI-IKD: High-Speed Accurate Off-Road Navigation using Learned Visual-Inertial Inverse Kinodynamics](https://mlsanity.com/paper/2203.15983/) (2022)
- [APPLD: Adaptive Planner Parameter Learning from Demonstration](https://mlsanity.com/paper/2004.00116/) (2020)
- [Benchmarking Metric Ground Navigation](https://mlsanity.com/paper/2008.13315/) (2020)
- [APPL: Adaptive Planner Parameter Learning](https://mlsanity.com/paper/2105.07620/) (2021)
- [CLUTR: Curriculum Learning via Unsupervised Task Representation Learning](https://mlsanity.com/paper/2210.10243/) (2022)
- [APPLI: Adaptive Planner Parameter Learning From Interventions](https://mlsanity.com/paper/2011.00400/) (2020)
- [Benchmarking Reinforcement Learning Techniques for Autonomous Navigation](https://mlsanity.com/paper/2210.04839/) (2022)
- [Curriculum-Based Reinforcement Learning for Quadrupedal Jumping: A Reference-free Design](https://mlsanity.com/paper/2401.16337/) (2024)
- [APPLR: Adaptive Planner Parameter Learning from Reinforcement](https://mlsanity.com/paper/2011.00397/) (2020)
- [APPLE: Adaptive Planner Parameter Learning from Evaluative Feedback](https://mlsanity.com/paper/2108.09801/) (2021)
- [High-Speed Accurate Robot Control using Learned Forward Kinodynamics and Non-linear Least Squares Optimization](https://mlsanity.com/paper/2206.08487/) (2022)
- [Learning to Model and Plan for Wheeled Mobility on Vertically Challenging Terrain](https://mlsanity.com/paper/2306.11611/) (2023)
- [Dexterous Legged Locomotion in Confined 3D Spaces with Reinforcement Learning](https://mlsanity.com/paper/2403.03848/) (2024)
- [Terrain-Attentive Learning for Efficient 6-DoF Kinodynamic Modeling on Vertically Challenging Terrain](https://mlsanity.com/paper/2403.16419/) (2024)
- [Reinforcement Learning for Wheeled Mobility on Vertically Challenging Terrain](https://mlsanity.com/paper/2409.02383/) (2024)
- [Motion Memory: Leveraging Past Experiences to Accelerate Future Motion Planning](https://mlsanity.com/paper/2310.06198/) (2023)
- [Team Orienteering Coverage Planning with Uncertain Reward](https://mlsanity.com/paper/2105.03721/) (2021)
- [Team Coordination on Graphs with State-Dependent Edge Cost](https://mlsanity.com/paper/2303.11457/) (2023)
- [Automatic Curriculum Learning with Gradient Reward Signals](https://mlsanity.com/paper/2312.13565/) (2023)
- [Scaling Team Coordination on Graphs with Reinforcement Learning](https://mlsanity.com/paper/2403.05787/) (2024)
- [Team Coordination on Graphs: Problem, Analysis, and Algorithms](https://mlsanity.com/paper/2403.15946/) (2024)

## Built on by (most cited first, 3 of 3)

- [Trajectory-Level Automatic Curriculum Learning for Legged Locomotion on Unstructured Terrain](https://mlsanity.com/paper/2608.16164/) (2026)
- [Active Curriculum Refinement for Reinforcement Learning](https://mlsanity.com/paper/2608.26469/) (2026)
- [GaitSpan: Growing Humanoid Locomotion from Walking to Running](https://mlsanity.com/paper/2607.12114/) (2026)

## 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.