# Learning to Walk and Fly with Adversarial Motion Priors

arXiv: https://arxiv.org/abs/2309.12784 · HTML: https://mlsanity.com/paper/2309.12784/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2023.

## Built on by (most cited first, 5 of 5)

- [InterReal: A Unified Physics-Based Imitation Framework for Learning Human-Object Interaction Skills](https://mlsanity.com/paper/2603.07516/) (2026)
- [Learning Aerodynamics for the Control of Flying Humanoid Robots](https://mlsanity.com/paper/2506.00305/) (2025)
- [Guiding Energy-Efficient Locomotion through Impact Mitigation Rewards](https://mlsanity.com/paper/2510.09543/) (2025)
- [Learning Energy-Efficient Air--Ground Actuation for Hybrid Robots on Stair-Like Terrain](https://mlsanity.com/paper/2603.26687/) (2026)
- [Multi-Gait Learning for Humanoid Robots Using Reinforcement Learning with Selective Adversarial Motion Prior](https://mlsanity.com/paper/2604.19102/) (2026)

## Abstract (arXiv)

Robot multimodal locomotion encompasses the ability to transition between walking and flying, representing a significant challenge in robotics. This work presents an approach that enables automatic smooth transitions between legged and aerial locomotion. Leveraging the concept of Adversarial Motion Priors, our method allows the robot to imitate motion datasets and accomplish the desired task without the need for complex reward functions. The robot learns walking patterns from human-like gaits and aerial locomotion patterns from motions obtained using trajectory optimization. Through this process, the robot adapts the locomotion scheme based on environmental feedback using reinforcement learning, with the spontaneous emergence of mode-switching behavior. The results highlight the potential for achieving multimodal locomotion in aerial humanoid robotics through automatic control of walking and flying modes, paving the way for applications in diverse domains such as search and rescue, surveillance, and exploration missions. This research contributes to advancing the capabilities of aerial humanoid robots in terms of versatile locomotion in various environments.