Go1 Adaptation

The go1 branch adds the RobotLab-Go1-v0 IsaacLab task and a matching MuJoCo deployment. It uses the complete Unitree Go1 URDF/MuJoCo model.

Interface

Policy joint order: FR, FL, RR, RL, with hip, thigh, calf for each leg.
Single-frame observation: 45 values in this order: body angular velocity (3), projected gravity (3), velocity command (3), relative joint position (12), joint velocity (12), previous action (12).
Actor history: 10 frames, or 450 values.
Action: 12 normalized joint-position offsets, scaled by 0.25 rad and added to the default joint angles.
Policy frequency: 50 Hz (0.005 s physics step and decimation 4).
PD gains: Kp=28, Kd=0.7; torque is limited to 33.5 Nm.
Training randomizes actuator delay over 0-4 physics steps (0-20 ms). The MuJoCo config defaults to zero extra delay because its delay setting is in 20 ms policy steps.

Train

Install the editable packages as described in the main README, then run:

cd go2_rl_robotlab
python scripts/rsl_rl/train.py \
  --task=RobotLab-Go1-v0 \
  --num_envs=4096 \
  --max_iterations=5000 \
  --headless

Play And Export

play.py exports both policy.pt and policy.onnx into the checkpoint run's exported/ directory before starting the rollout.

python scripts/rsl_rl/play.py \
  --task=RobotLab-Go1-v0 \
  --num_envs=64 \
  --checkpoint=/absolute/path/to/model_5000.pt

MuJoCo

Place the exported TorchScript file at deploy/pre_train/go1/policy.pt, or edit policy_path in deploy/deploy_mujoco/configs/go1.yaml. Then run:

MUJOCO_GL=glfw python deploy/deploy_mujoco/deploy_go1.py

Set xml_path in go1.yaml to select flat.xml, stairs.xml, boxes.xml, or stairs_and_slope.xml.

Actuator Assumption

The Go1 asset publishes a 33.5 Nm effort limit but does not include measured torque-speed knee points. The training actuator therefore uses the known effort limit with delayed PD control instead of reusing the Go2-HV torque-speed curve.

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