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700dd75 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 | # Installation (Training)
This guide walks through setting up the SONIC training environment for whole-body humanoid control.
## Prerequisites
- **GPU**: NVIDIA GPU with CUDA 12.x (L40 recommended)
- **OS**: Ubuntu 22.04+
- **Python**: 3.11 (required by Isaac Lab; sim/teleop/deploy scripts work on 3.10+)
- **Isaac Lab**: 2.3+ (required for simulation environments)
## Install Isaac Lab
SONIC training uses Isaac Lab for physics simulation. Follow the official
[Isaac Lab installation guide](https://isaac-sim.github.io/IsaacLab/main/source/setup/installation/index.html)
to install Isaac Lab.
After installation, verify:
```bash
python -c "import isaaclab; print(isaaclab.__version__)"
```
## Install gear_sonic (Training)
From the repository root:
```bash
pip install -e "gear_sonic/[training]"
```
This installs the training dependencies (Hydra, W&B, HuggingFace TRL, etc.)
on top of the Isaac Lab environment.
## Download Model and Data from Hugging Face
SONIC model checkpoints and SMPL motion data are hosted on
[Hugging Face](https://huggingface.co/nvidia/GEAR-SONIC).
```bash
pip install huggingface_hub
python download_from_hf.py --training
```
This downloads:
- **PyTorch checkpoint** (`sonic_release/last.pt`) for finetuning
- **SMPL motion data** (`data/smpl_filtered/`) for the SMPL encoder
## Prepare Robot Motion Data
SONIC trains on the [Bones-SEED](https://huggingface.co/datasets/bones-studio/seed) motion capture dataset
(142K+ motion sequences retargeted to the Unitree G1).
### Step 1: Download and convert
Download the **G1 retargeted CSVs** (29 DOF, 120 FPS) from
[Bones-SEED on HuggingFace](https://huggingface.co/datasets/bones-studio/seed), then convert:
```bash
python gear_sonic/data_process/convert_soma_csv_to_motion_lib.py \
--input /path/to/bones_seed/g1/csv/ \
--output data/motion_lib_bones_seed/robot \
--fps 30 --fps_source 120 --individual --num_workers 16
```
### Step 2: Filter motions
Remove motions the G1 robot cannot perform:
```bash
python gear_sonic/data_process/filter_and_copy_bones_data.py \
--source data/motion_lib_bones_seed/robot \
--dest data/motion_lib_bones_seed/robot_filtered --workers 16
```
This removes ~8.7% of motions (~130K of 142K remain). See the
[Training Guide](../user_guide/training.md) for details.
Your data directory should look like:
```
<repo_root>/
├── data/
│ ├── motion_lib_bones_seed/
│ │ └── robot_filtered/ # Filtered G1 motions (~130K PKLs)
│ └── smpl_filtered/ # SMPL motion data (from Hugging Face)
└── sonic_release/ # Released checkpoint (from Hugging Face)
```
> **Note**: Data processing scripts (`gear_sonic/data_process/`) do **not** require
> Isaac Lab and can be run on any machine with `pip install -e gear_sonic/`.
## Verify Installation
First, run the pre-flight check to verify all dependencies:
```bash
python check_environment.py --training
```
Then run a quick smoke test with a small number of environments:
```bash
# Interactive (with viewer)
python gear_sonic/train_agent_trl.py \
+exp=manager/universal_token/all_modes/sonic_release \
num_envs=16 headless=False \
++algo.config.num_learning_iterations=5
# Headless (server / no display)
python gear_sonic/train_agent_trl.py \
+exp=manager/universal_token/all_modes/sonic_release \
num_envs=16 headless=True \
++algo.config.num_learning_iterations=5
```
After a minute of initialization you should see training metrics (rewards, errors)
printing to the console.
## Full Training
Once installation is verified, see the [Training Guide](../user_guide/training.md)
for full training commands (64+ GPU recommended), evaluation, ONNX export, and
SOMA encoder setup.
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