To train an AI model for your robot, you need a robotics dataset. For that, you need to have first recorded a dataset.

Don’t have a dataset? Use one of ours: phospho-ai/so100-tictactoe

​
Train GR00T-N1-2B in one click from the phosphobot dashboard

You can fine-tune Nvidia GR00T-N1-2B on your dataset right from the phosphobot dashboard. This is the easiest way to train a AI robotics model.

  1. Launch the phosphobot server and go to the phosphobot dashboard in your browser: http://localhost
phosphobot run
  1. Create a phospho account or log in by clicking on the Sign in button in the top right corner.
  2. (If not already done) Add your Hugging Face token in the Admin Settings tab with Write authorization. This will sync your datasets to Hugging Face. Then, record a dataset using teleoperation. Read the full guide here.

Garbage in, garbage out. Our tests show that training works with about 30 episodes. It’s better for the task to be specific. Have good lighting and similar setup.

  1. In the AI Training and Control section, enter the the name of your dataset on Hugging Face (example: PLB/simple-lego-pickup-mono-2).

  1. Hit the Train AI Model button. Your model starts training. Training can take up to 3 hours. Follow the training using the button View trained models.

Your trained model is uploaded to HuggingFace on the phospho-app account. Its name is something like phospho-app/YOUR_DATASET_NAME-A_RANDOM_ID.

Next up, you can start controlling your robot with the trained model.

​
How to train the ACT (Action Chunking Transformer) model with LeRobot?

The ACT model is a transformer-based model that learns to chunk actions in a sequence. It is trained on a dataset of action sequences and their corresponding chunked actions.

LeRobot is a research-oriented library by Hugging Face that provides a simple interface to train AI models. It is still a work in progress, but it is already very powerful.

Follow our guide to train the ACT model on your dataset.

Train your ACT model with LeRobot

Train your ACT model with LeRobot

​
How to train the Pi0 (Pi-Zero) model with the SO-100 robot arm?

Pi0 is a powerful VLA (Vision Language Action model) by Physical Intelligence. They released an open weight model that can be trained on your own dataset.

To train the Pi0 model, you need to use the openpi repository with a few tweaks for the SO-100 robot arm compatibility. We added support for the SO-100 arm in this fork of the openpi repository.

  1. Clone our custom openpi repository and install it:
git clone https://github.com/phospho-app/openpi.git
cd openpi
  1. Install UV if you don’t have it already:
curl -LsSf https://astral.sh/uv/install.sh | sh
source $HOME/.local/bin/env
  1. Setup the environment using uv:
GIT_LFS_SKIP_SMUDGE=1 uv sync
  1. (Optional) If you want to use Weights & Biases for tracking training metrics, log in with:
# Get your auth token at https://wandb.ai/authorize
uv run wandb login
  1. Edit the config file src/openpi/training/config.py to change the dataset to your own.

By default, the training config uses the PLB/Orange-brick-in-black-box SO-100 dataset.

Make sure you dataset has a correct task description for each task in the dataset. Eg: in the PLB/Orange-brick-in-black-box dataset, it’s “Put the orange brick in the black pot”

  1. Start the training:
uv run scripts/compute_norm_stats.py --config-name pi0_so100
# Main train command in nohup, it will take a while
XLA_PYTHON_CLIENT_MEM_FRACTION=0.9 uv run scripts/train.py pi0_so100 --exp-name=pi0_so100_blackbox --overwrite

You need a GPU with at least 70GB of memory to train the pi0 model. We recommend using a A100 (80GB) or a H100.

  1. Once training is done, push your model to Hugging Face:
huggingface-cli login  # Follow the authentication steps
huggingface-cli repo create your-model-name --type model

# Push direclty using the cli
huggingface-cli upload USERNAME/your-model-name \
  ~/path/to/folder/ \
  --repo-type model

​
Next steps

Test the model you just trained on your robot. See the Use AI models page for more information.

Use a robotics AI model

Let a trained AI model control your robot

To train an AI model for your robot, you need a robotics dataset. For that, you need to have first recorded a dataset.

Don’t have a dataset? Use one of ours: phospho-ai/so100-tictactoe

​
Train GR00T-N1-2B in one click from the phosphobot dashboard

You can fine-tune Nvidia GR00T-N1-2B on your dataset right from the phosphobot dashboard. This is the easiest way to train a AI robotics model.

  1. Launch the phosphobot server and go to the phosphobot dashboard in your browser: http://localhost
phosphobot run
  1. Create a phospho account or log in by clicking on the Sign in button in the top right corner.
  2. (If not already done) Add your Hugging Face token in the Admin Settings tab with Write authorization. This will sync your datasets to Hugging Face. Then, record a dataset using teleoperation. Read the full guide here.

Garbage in, garbage out. Our tests show that training works with about 30 episodes. It’s better for the task to be specific. Have good lighting and similar setup.

  1. In the AI Training and Control section, enter the the name of your dataset on Hugging Face (example: PLB/simple-lego-pickup-mono-2).

  1. Hit the Train AI Model button. Your model starts training. Training can take up to 3 hours. Follow the training using the button View trained models.

Your trained model is uploaded to HuggingFace on the phospho-app account. Its name is something like phospho-app/YOUR_DATASET_NAME-A_RANDOM_ID.

Next up, you can start controlling your robot with the trained model.

​
How to train the ACT (Action Chunking Transformer) model with LeRobot?

The ACT model is a transformer-based model that learns to chunk actions in a sequence. It is trained on a dataset of action sequences and their corresponding chunked actions.

LeRobot is a research-oriented library by Hugging Face that provides a simple interface to train AI models. It is still a work in progress, but it is already very powerful.

Follow our guide to train the ACT model on your dataset.

Train your ACT model with LeRobot

Train your ACT model with LeRobot

​
How to train the Pi0 (Pi-Zero) model with the SO-100 robot arm?

Pi0 is a powerful VLA (Vision Language Action model) by Physical Intelligence. They released an open weight model that can be trained on your own dataset.

To train the Pi0 model, you need to use the openpi repository with a few tweaks for the SO-100 robot arm compatibility. We added support for the SO-100 arm in this fork of the openpi repository.

  1. Clone our custom openpi repository and install it:
git clone https://github.com/phospho-app/openpi.git
cd openpi
  1. Install UV if you don’t have it already:
curl -LsSf https://astral.sh/uv/install.sh | sh
source $HOME/.local/bin/env
  1. Setup the environment using uv:
GIT_LFS_SKIP_SMUDGE=1 uv sync
  1. (Optional) If you want to use Weights & Biases for tracking training metrics, log in with:
# Get your auth token at https://wandb.ai/authorize
uv run wandb login
  1. Edit the config file src/openpi/training/config.py to change the dataset to your own.

By default, the training config uses the PLB/Orange-brick-in-black-box SO-100 dataset.

Make sure you dataset has a correct task description for each task in the dataset. Eg: in the PLB/Orange-brick-in-black-box dataset, it’s “Put the orange brick in the black pot”

  1. Start the training:
uv run scripts/compute_norm_stats.py --config-name pi0_so100
# Main train command in nohup, it will take a while
XLA_PYTHON_CLIENT_MEM_FRACTION=0.9 uv run scripts/train.py pi0_so100 --exp-name=pi0_so100_blackbox --overwrite

You need a GPU with at least 70GB of memory to train the pi0 model. We recommend using a A100 (80GB) or a H100.

  1. Once training is done, push your model to Hugging Face:
huggingface-cli login  # Follow the authentication steps
huggingface-cli repo create your-model-name --type model

# Push direclty using the cli
huggingface-cli upload USERNAME/your-model-name \
  ~/path/to/folder/ \
  --repo-type model

​
Next steps

Test the model you just trained on your robot. See the Use AI models page for more information.

Use a robotics AI model

Let a trained AI model control your robot