# Trajectory-Level Automatic Curriculum Learning for Legged Locomotion on Unstructured Terrain

arXiv: https://arxiv.org/abs/2608.16164 · HTML: https://mlsanity.com/paper/2608.16164/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 38 references that are in the map.
- First posted on arXiv 17 Aug 2026.

## Builds on (most cited first, 30 of 31)

- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [Learning Quadrupedal Locomotion over Challenging Terrain](https://mlsanity.com/paper/2010.11251/) (2020)
- [Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning](https://mlsanity.com/paper/2109.11978/) (2021)
- [RMA: Rapid Motor Adaptation for Legged Robots](https://mlsanity.com/paper/2107.04034/) (2021)
- [Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design](https://mlsanity.com/paper/2012.02096/) (2020)
- [Prioritized Level Replay](https://mlsanity.com/paper/2010.03934/) (2020)
- [Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions](https://mlsanity.com/paper/1901.01753/) (2019)
- [Evolving Curricula with Regret-Based Environment Design](https://mlsanity.com/paper/2203.01302/) (2022)
- [Rapid Locomotion via Reinforcement Learning](https://mlsanity.com/paper/2205.02824/) (2022)
- [Extreme Parkour with Legged Robots](https://mlsanity.com/paper/2309.14341/) (2023)
- [ANYmal Parkour: Learning Agile Navigation for Quadrupedal Robots](https://mlsanity.com/paper/2306.14874/) (2023)
- [Automatic Curriculum Learning For Deep RL: A Short Survey](https://mlsanity.com/paper/2003.04664/) (2020)
- [Robot Parkour Learning](https://mlsanity.com/paper/2309.05665/) (2023)
- [Teacher algorithms for curriculum learning of Deep RL in continuously parameterized environments](https://mlsanity.com/paper/1910.07224/) (2019)
- [Replay-Guided Adversarial Environment Design](https://mlsanity.com/paper/2110.02439/) (2021)
- [Humanoid Parkour Learning](https://mlsanity.com/paper/2406.10759/) (2024)
- [Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion](https://mlsanity.com/paper/2401.17583/) (2024)
- [ALLSTEPS: Curriculum-driven Learning of Stepping Stone Skills](https://mlsanity.com/paper/2005.04323/) (2020)
- [BeamDojo: Learning Agile Humanoid Locomotion on Sparse Footholds](https://mlsanity.com/paper/2502.10363/) (2025)
- [Attention-Based Map Encoding for Learning Generalized Legged Locomotion](https://mlsanity.com/paper/2506.09588/) (2025)
- [PIE: Parkour with Implicit-Explicit Learning Framework for Legged Robots](https://mlsanity.com/paper/2408.13740/) (2024)
- [Actor Prioritized Experience Replay](https://mlsanity.com/paper/2209.00532/) (2022)
- [High-speed control and navigation for quadrupedal robots on complex and discrete terrain](https://mlsanity.com/paper/2506.02835/) (2025)
- [Curriculum-Based Reinforcement Learning for Quadrupedal Jumping: A Reference-free Design](https://mlsanity.com/paper/2401.16337/) (2024)
- [Guided Curriculum Learning for Walking Over Complex Terrain](https://mlsanity.com/paper/2010.03848/) (2020)
- [MARG: MAstering Risky Gap Terrains for Legged Robots with Elevation Mapping](https://mlsanity.com/paper/2509.20036/) (2025)
- [Learning Progress Driven Multi-Agent Curriculum](https://mlsanity.com/paper/2205.10016/) (2022)
- [VertiSelector: Automatic Curriculum Learning for Wheeled Mobility on Vertically Challenging Terrain](https://mlsanity.com/paper/2409.17469/) (2024)
- [HACL: History-Aware Curriculum Learning for Fast Locomotion](https://mlsanity.com/paper/2505.18429/) (2025)
- [GACL: Grounded Adaptive Curriculum Learning with Active Task and Performance Monitoring](https://mlsanity.com/paper/2508.02988/) (2025)

## Built on by (most cited first, 1 of 1)

- [Predictive Safety Curricula for Robust Legged Locomotion](https://mlsanity.com/paper/2609.37070/) (2026)

## Abstract (arXiv)

Training locomotion policies for complex unstructured terrain requires a curriculum to avoid early exploration failures. However, since unstructured terrain lacks explicit difficulty ordering for curriculum design, existing methods resort to heuristic curricula over parameterized terrains. This abstraction limits generalization, as policies can overadapt to near-fixed perceptual patterns. To address this, we propose textbfourname, an Trajectory-level Automatic Curriculum Learning framework that generates training tasks directly from unstructured terrain maps. At each curriculum update, the evaluator learns a difficulty function for the current policy that maps a given trajectory task to a difficulty score. The sampler then proposes new trajectories guided by the learned evaluator as the curriculum for the next policy update. This forms a closed loop in which the curriculum is iteratively matched to the evolving policy. Quantitative and qualitative experiments show that ourname continuously provides effective curricula on unstructured terrain, improving trajectory success rate by (56.3%) over direct training without curriculum. Compared with handcrafted curriculum learning, our method improves success rate by (18.5%) on the hardest terrain tasks and by up to (39.74%) when evaluating traversal from diverse approach directions on the same obstacle type.