Sim2Real Reinforcement Learning for the Unitree G1
Learn the complete workflow for building, training, and deploying locomotion policies using NVIDIA Isaac Sim, Isaac Lab, and RSL-RL — then transfer those skills directly to a real Unitree G1 quadruped. This course gives you a practical, end-to-end sim-to-real pipeline, from world building and sensors to PPO training, ROS 2 integration, and real-robot deployment.
Start LearningThis course includes
- 15 step-by-step Sim2Real lessons
- Full Isaac Sim & Isaac Lab setup guidance
- PPO training with RSL-RL
- Policy deployment on Unitree Go1
About this course
This course teaches you how to build complete reinforcement-learning locomotion systems inside NVIDIA Isaac Sim and transfer those skills to a real Unitree Go1. You'll learn USD and PhysX fundamentals, create observations and action spaces, design rewards and termination rules, generate terrain curricula, and train PPO policies using RSL-RL’s high-performance vectorized environments. The final modules guide you through ROS 2 bridging, policy packaging, G1 bring-up, safety checks, deployment, and real-world tuning — giving you a full, practical sim-to-real workflow used in modern robotics labs.
Skills you'll gain
- Full Isaac Sim & Isaac Lab installation and configuration
- Working with USD, PhysX, and scene composition
- Building sensor pipelines and observation systems
- Creating action spaces, command generators, and scaling
- Designing rewards, terminations, and randomizations
- Generating terrains and difficulty curricula
- Training PPO locomotion agents with RSL-RL
- Using vectorized environments for high-speed RL
- Creating ROS 2 bridges and packaging policies
- Deploying trained policies on the Unitree G1
- Real-robot debugging, safety, and validation
