# 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:

```bash
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.

```bash
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:

```bash
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.