gr00t-n1-5-deploy-thor

name	gr00t-n1-5-deploy-thor
description	Fine-tune NVIDIA Isaac GR00T N1.5 for the LeRobot SO-101 arm and deploy on Jetson AGX Thor. Covers Thor flashing, dev environment setup, SO-ARM data collection, cloud training via NVIDIA Brev, and GR00T N1.5 inference on Thor. Requires Jetson AGX Thor and SO-101 arm hardware.

Fine-tune GR00T N1.5 for LeRobot SO-101 Arm and Deploy on Jetson Thor

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Execution model

Run one phase at a time. After each phase:

• Relay all command output to the user.
• If output contains [STOP] → stop immediately, consult the failure decision tree below.
• If output ends with [OK] → tell the user "Phase N complete" and proceed to the next phase.

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Prerequisites

Requirement	Details
Hardware	NVIDIA Jetson AGX Thor Developer Kit
Robot arm	LeRobot SO-101 (leader + follower)
Cameras	2× USB cameras (wrist + front)
Flashing	USB drive ≥16GB, monitor, DP/HDMI cable, 240W+ power, USB keyboard
Cloud training	GPU with ≥25GB VRAM (Ampere or newer — V100 not supported)
System image	Thor v38.2 (latest as of Sept 2025)

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Phase 1 — Flash Thor system image (~20 min)

Download the ISO image from https://developer.nvidia.com/embedded/jetpack/downloads (select Thor 38.2).

Create bootable USB with Balena Etcher (https://etcher.balena.io/):

1. Select the ISO image
2. Select the USB drive as target
3. Click Flash and wait for completion

Insert USB, keyboard, display cable, and power into Thor. Boot and select:

• Boot Manager → select USB drive → Esc → Continue
• Jetson Thor options → Flash Jetson AGX Thor Developer Kit on NVMe

Wait ~15 minutes for flashing, then wait for Update Progress to reach 100%.

[OK] when Ubuntu 24.04 initial setup screen appears. [STOP] if screen goes black — see FAQ in source.

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Phase 2 — Install development dependencies (~10–20 min)

Install JetPack SDK:

sudo apt update
sudo apt install nvidia-jetpack

Install additional tools:

sudo apt install firefox python3 python3-pip
sudo pip3 install -U pip
sudo pip3 install jetson-stats

Install Miniconda:

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-aarch64.sh
chmod +x Miniconda3-latest-Linux-aarch64.sh
./Miniconda3-latest-Linux-aarch64.sh
source ~/.bashrc
conda --version

For GPU PyTorch on Thor, download pre-compiled wheels (Python 3.10 + CUDA 13):

• PyTorch 2.9 and TorchVision 0.24 wheels from Seeed SharePoint links in references/source.body.md
• Additional wheels at https://pypi.jetson-ai-lab.io/sbsa/cu130

[OK] when conda --version and python3 -c "import torch; print(torch.cuda.is_available())" both succeed.

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Phase 3 — Data collection with SO-ARM

Follow the complete SO-ARM tutorial at: https://wiki.seeedstudio.com/lerobot_so100m_new/

Key steps:

1. Configure motors before assembly
2. Assemble leader and follower arms
3. Calibrate both arms (do NOT connect cameras during calibration)
4. Add cameras (wrist + front)
5. Record dataset episodes

On Thor, two USB cameras must be on different USB hub controllers. Use one USB-A port and one external USB-C hub.

[OK] when dataset is collected and saved locally.

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Phase 4 — Train GR00T N1.5 on cloud (~varies)

Set up NVIDIA Brev (https://login.brev.nvidia.com/signin) or equivalent cloud GPU.

On the cloud server, install Conda and clone Isaac-GR00T:

mkdir -p ~/miniconda3 && cd ~/miniconda3
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
bash miniconda.sh -b -u -p ~/miniconda3
source ~/miniconda3/bin/activate
conda init --all

git clone https://github.com/NVIDIA/Isaac-GR00T
cd Isaac-GR00T
conda create -n gr00t python=3.10
conda activate gr00t
pip install --upgrade setuptools
pip install -e .[base]
pip install --no-build-isolation flash-attn==2.7.1.post4

Fine-tune:

python scripts/gr00t_finetune.py \
   --dataset-path ./demo_data/so101-table-cleanup/ \
   --num-gpus 1 \
   --output-dir ./so101-checkpoints \
   --max-steps 10000 \
   --data-config so100_dualcam \
   --video-backend torchvision_av

Default fine-tuning requires ~25GB VRAM. Add --no-tune_diffusion_model to reduce VRAM usage.

Download the trained checkpoint from the cloud server.

[OK] when checkpoint files are available locally on Thor.

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Phase 5 — Run GR00T N1.5 inference on Thor

Pull the reference Docker image (or use your own environment):

docker pull johnnync/isaac-gr00t:r38.2.arm64-sbsa-cu130-24.04

Start the container:

sudo docker run --rm -it \
  --network=host \
  -e NVIDIA_DRIVER_CAPABILITIES=compute,utility,video,graphics \
  --runtime nvidia \
  --privileged \
  -v /tmp/.X11-unix:/tmp/.X11-unix \
  -v /etc/X11:/etc/X11 \
  --device /dev/nvhost-vic \
  -v /dev:/dev \
  johnnync/isaac-gr00t:r38.2.arm64-sbsa-cu130-24.04

Inside the container, install GR00T:

git clone https://github.com/NVIDIA/Isaac-GR00T.git
cd Isaac-GR00T
pip install --upgrade setuptools
pip install -e .[thor]

Terminal 1 — Start inference server:

python scripts/inference_service.py --server \
    --model_path ./so101-checkpoints \
    --embodiment-tag new_embodiment \
    --data-config so100_dualcam \
    --denoising-steps 4

Terminal 2 — Start inference client (in same container via docker exec):

python examples/SO-100/eval_lerobot.py \
    --robot.type=so100_follower \
    --robot.port=/dev/ttyACM0 \
    --robot.id=my_awesome_follower_arm \
    --robot.cameras="{ wrist: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}, front: {type: opencv, index_or_path: 2, width: 640, height: 480, fps: 30}}" \
    --policy_host=0.0.0.0 \
    --lang_instruction="Grab pens and place into pen holder."

Replace index_or_path with your camera indices (find with ls /dev/video*).

[OK] when the robot arm executes the learned behavior.

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Failure decision tree

Symptom	Action
Thor screen goes black during flash	Enable SoC Display Hand-Off in UEFI setup menu. Flashing continues in background.
No HDMI signal after flash	Switch to DisplayPort (DP) connector.
No keyboard input during flash	Use a wired USB keyboard. Wireless keyboards may not work.
KVM no video output	Connect monitor directly to Thor, bypassing KVM.
Cloud fine-tuning "GPU not supported"	Use Ampere or newer GPU (RTX A6000, RTX 4090). V100 is not supported.
/dev/ttyACM0 not found for arm	Thor may lack CH34x drivers. Install from https://github.com/juliagoda/CH341SER
Type-C hub not recognized	Use the Type-C port closest to the QSFP28 connector.
OOM during fine-tuning	Add --no-tune_diffusion_model flag. Or use a GPU with more VRAM.
Two USB cameras can't stream simultaneously	Cameras must be on different USB hub controllers. Use USB-A + external USB-C hub.
ACT inference fails in GR00T Docker image	The GR00T Docker image (Python 3.12) doesn't support LeRobot ACT inference. Use external environment.
User password incorrect after flash with capture card	Keyboard input bug with capture cards. Re-flash and verify password carefully.

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Reference files

• references/source.body.md — full original Seeed tutorial with Thor flashing screenshots, Docker image pip list, Brev platform walkthrough, and FAQ (reference only)