Training a Unitree G1 to Walk w/ Reinforcement Learning
sentdex
Dec 19, 2025
The creator successfully bridges the sim-to-real gap for a Unitree G1 humanoid by abandoning implicit PD controllers for an explicit Python-based approach. This shift ensures parity between simulation and real-world performance, enabling the robot to navigate off-road terrain independently. The focus is now shifting toward modular policies for complex tasks like object manipulation.
Key insight: The author achieves stable sim-to-real locomotion on a UniTree G1 using a neural network with fewer than 200,000 parameters, proving that massive scale isn't always the prerequisite for physical stability.
