# Learning Bipedal Walking on a Quadruped Robot via Adversarial Motion Priors

arXiv: https://arxiv.org/abs/2407.02282 · HTML: https://mlsanity.com/paper/2407.02282/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2024.

## Built on by (most cited first, 4 of 4)

- [Humanoid Whole-Body Locomotion on Narrow Terrain via Dynamic Balance and Reinforcement Learning](https://mlsanity.com/paper/2502.17219/) (2025)
- [Gait-Conditioned Reinforcement Learning with Multi-Phase Curriculum for Humanoid Locomotion](https://mlsanity.com/paper/2505.20619/) (2025)
- [TAR: Teacher-Aligned Representations via Contrastive Learning for Quadrupedal Locomotion](https://mlsanity.com/paper/2503.20839/) (2025)
- [Actuator Dynamics Curricula for Narrow-Viability Tasks in Legged Robot Learning](https://mlsanity.com/paper/2609.09492/) (2026)

## Abstract (arXiv)

Previous studies have successfully demonstrated agile and robust locomotion in challenging terrains for quadrupedal robots. However, the bipedal locomotion mode for quadruped robots remains unverified. This paper explores the adaptation of a learning framework originally designed for quadrupedal robots to operate blind locomotion in biped mode. We leverage a framework that incorporates Adversarial Motion Priors with a teacher-student policy to enable imitation of a reference trajectory and navigation on tough terrain. Our work involves transferring and evaluating a similar learning framework on a quadruped robot in biped mode, aiming to achieve stable walking on both flat and complicated terrains. Our simulation results demonstrate that the trained policy enables the quadruped robot to navigate both flat and challenging terrains, including stairs and uneven surfaces.