# Learning Velocity-based Humanoid Locomotion: Massively Parallel Learning with Brax and MJX

arXiv: https://arxiv.org/abs/2407.05148 · HTML: https://mlsanity.com/paper/2407.05148/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2024.

## Built on by (most cited first, 2 of 2)

- [Learning Skateboarding for Humanoid Robots through Massively Parallel Reinforcement Learning](https://mlsanity.com/paper/2409.07846/) (2024)
- [MyoInteract: A Framework for Fast Prototyping of Biomechanical HCI Tasks using Reinforcement Learning](https://mlsanity.com/paper/2602.15245/) (2026)

## Abstract (arXiv)

Humanoid locomotion is a key skill to bring humanoids out of the lab and into the real-world. Many motion generation methods for locomotion have been proposed including reinforcement learning (RL). RL locomotion policies offer great versatility and generalizability along with the ability to experience new knowledge to improve over time. This work presents a velocity-based RL locomotion policy for the REEM-C robot. The policy uses a periodic reward formulation and is implemented in Brax/MJX for fast training. Simulation results for the policy are demonstrated with future experimental results in progress.