Training a Unitree G1 to Walk with Reinforcement Learning

Learn to train a Unitree G1 humanoid robot to walk using reinforcement learning techniques in this 43-minute tutorial. Explore the implementation of Proximal Policy Optimization (PPO) algorithm combined with mjlab simulation environment to develop walking capabilities for the humanoid robot. Discover how to set up the training environment, configure the reward systems, and optimize the learning process to achieve stable walking behavior both in indoor and outdoor scenarios. Master the practical application of deep reinforcement learning for robotics locomotion, understanding the challenges and solutions involved in teaching a bipedal robot to navigate different terrains and environments effectively.