# Teacher Motion Priors: Enhancing Robot Locomotion over Challenging Terrain

arXiv: https://arxiv.org/abs/2504.10390 · HTML: https://mlsanity.com/paper/2504.10390/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 33 references that are in the map.
- First posted on arXiv 14 Apr 2025.

## Builds on (most cited first, 20 of 20)

- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [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)
- [Learning agile and dynamic motor skills for legged robots](https://mlsanity.com/paper/1901.08652/) (2019)
- [Reinforcement Learning with Unsupervised Auxiliary Tasks](https://mlsanity.com/paper/1611.05397/) (2016)
- [Learning to Walk via Deep Reinforcement Learning](https://mlsanity.com/paper/1812.11103/) (2018)
- [Sim-to-Real Reinforcement Learning for Deformable Object Manipulation](https://mlsanity.com/paper/1806.07851/) (2018)
- [Deep Whole-Body Control: Learning a Unified Policy for Manipulation and Locomotion](https://mlsanity.com/paper/2210.10044/) (2022)
- [Adversarial Motion Priors Make Good Substitutes for Complex Reward Functions](https://mlsanity.com/paper/2203.15103/) (2022)
- [Loss is its own Reward: Self-Supervision for Reinforcement Learning](https://mlsanity.com/paper/1612.07307/) (2016)
- [Advancing Humanoid Locomotion: Mastering Challenging Terrains with Denoising World Model Learning](https://mlsanity.com/paper/2408.14472/) (2024)
- [Humanoid-Gym: Reinforcement Learning for Humanoid Robot with Zero-Shot Sim2Real Transfer](https://mlsanity.com/paper/2404.05695/) (2024)
- [The MIT Humanoid Robot: Design, Motion Planning, and Control For Acrobatic Behaviors](https://mlsanity.com/paper/2104.09025/) (2021)
- [CTS: Concurrent Teacher-Student Reinforcement Learning for Legged Locomotion](https://mlsanity.com/paper/2405.10830/) (2024)
- [Feedback Control of a Cassie Bipedal Robot: Walking, Standing, and Riding a Segway](https://mlsanity.com/paper/1809.07279/) (2018)
- [Learning to Open and Traverse Doors with a Legged Manipulator](https://mlsanity.com/paper/2409.04882/) (2024)
- [Learning Robust, Agile, Natural Legged Locomotion Skills in the Wild](https://mlsanity.com/paper/2304.10888/) (2023)
- [Dynamic Walking with Compliance on a Cassie Bipedal Robot](https://mlsanity.com/paper/1904.11104/) (2019)
- [Heuristic Planning for Rough Terrain Locomotion in Presence of External Disturbances and Variable Perception Quality](https://mlsanity.com/paper/1805.10238/) (2018)

## Abstract (arXiv)

Achieving robust locomotion on complex terrains remains a challenge due to high dimensional control and environmental uncertainties. This paper introduces a teacher prior framework based on the teacher student paradigm, integrating imitation and auxiliary task learning to improve learning efficiency and generalization. Unlike traditional paradigms that strongly rely on encoder-based state embeddings, our framework decouples the network design, simplifying the policy network and deployment. A high performance teacher policy is first trained using privileged information to acquire generalizable motion skills. The teacher's motion distribution is transferred to the student policy, which relies only on noisy proprioceptive data, via a generative adversarial mechanism to mitigate performance degradation caused by distributional shifts. Additionally, auxiliary task learning enhances the student policy's feature representation, speeding up convergence and improving adaptability to varying terrains. The framework is validated on a humanoid robot, showing a great improvement in locomotion stability on dynamic terrains and significant reductions in development costs. This work provides a practical solution for deploying robust locomotion strategies in humanoid robots.