Rl Humanoid Locomotion

Learn why humanoid locomotion is hard, MDP formulation for bipedal walking, and how to set up Isaac Lab environment for Unitree G1.

Detailed analysis of 12+ reward terms for humanoid walking, reward hacking avoidance, and reward curriculum from standing to walking.

Step-by-step tutorial to train a walking policy for Unitree G1 with PPO in Isaac Lab, from config to evaluation.

Extend G1 walking policy to complex terrain with curriculum learning, domain randomization, and teacher-student training.

Train locomotion for the full-size Unitree H1, compare with G1, and adjust rewards and PD gains for a 1.8m humanoid.

Extend H1 policy to running at 2+ m/s, sharp turning, lateral walking, and a multi-gait command-conditioned policy.

Explore H1-2 with dexterous hands and improved actuators, how hardware changes affect locomotion, and loco-manipulation basics.

Combine locomotion and manipulation: carry boxes, push carts, open doors while walking, with multi-objective reward design.

Parkour for humanoid robots — jumping obstacles, climbing stairs, navigating rough terrain with RL and terrain perception.

Complete pipeline for deploying RL locomotion policies to real humanoid robots — domain randomization, system ID, safety, and Unitree SDK.