Nirman
cagataydev

cagataydev/sac-unitree-go2-mujoco

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Model Documentation

SAC Unitree Go2 — MuJoCo Locomotion Policy



A Soft Actor-Critic (SAC) policy trained to make the Unitree Go2 quadruped walk forward in MuJoCo simulation.

Trained entirely on a MacBook (CPU, no GPU, no Isaac Gym) using strands-robots.

Results



| Metric | Value | |--------|-------| | Algorithm | SAC (Soft Actor-Critic) | | Training steps | 1.74M | | Training time | ~40 min (MacBook M-series, CPU) | | Parallel envs | 8 | | Network | MLP [256, 256] | | Best reward | 4,912 | | Mean distance | 21 meters per episode | | Forward velocity | ~1 m/s | | Episode length | 1,000/1,000 (full episodes) |

Demo Video





Usage



python
from stable_baselines3 import SAC

model = SAC.load("best/best_model")

In a MuJoCo Go2 environment:

obs, _ = env.reset()
for _ in range(1000):
    action, _ = model.predict(obs, deterministic=True)
    obs, reward, done, truncated, info = env.step(action)


Reward Function




reward = forward_vel × 5.0       
primary: move forward

       + alive_bonus × 1.0       
stay upright

       + upright_reward × 0.3    
orientation bonus

       
  • ctrl_cost × 0.001       
    minimize energy
    • 
       
  • lateral_penalty × 0.3   
    don't drift sideways
    • 
       
  • smoothness × 0.0001     
    discourage jerky motion
    •


    Why SAC > PPO



    PPO (500K steps): Go2 learned to stand still. Reward = 615, distance = 0.02m. SAC (1.74M steps): Go2 walks 21 meters. Reward = 4,912.

    SAC's off-policy learning + entropy regularization explores more effectively in continuous action spaces.

    Files



  • best/best_model.zip — Best checkpoint (highest eval reward)
  • checkpoints/ — All 100K-step checkpoints
  • logs/evaluations.npz — Evaluation metrics over training
  • go2_walking.mp4 — Demo video


    • Environment



  • Simulator: MuJoCo (via mujoco-python)
  • Robot: Unitree Go2 (12 DOF) from MuJoCo Menagerie
  • Observation: joint positions, velocities, torso orientation, height (37-dim)
  • Action: joint torques (12-dim, continuous)


    • License



    Apache-2.0

    Files & Weights

    FilenameSizeAction