  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.
