Documentation Update

docs/operation.rst

@@ -8,3 +8,5 @@ Operation
   ./operation/mobile.rst
   ./operation/stationary.rst
   ./operation/data_collection.rst
+  ./operation/training.rst
+  ./operation/hugging_face.rst

docs/operation/hugging_face.rst

@@ -0,0 +1,127 @@
+==================
+Hugging Face Guide
+==================
+
+Uploading and Downloading Datasets on Hugging Face
+==================================================
+
+Creating an Account
+-------------------
+
+If you don't already have an account, sign up for a new account on the `Hugging Face Sign Up <https://huggingface.co/join>`_.
+
+Creating a New Dataset Repository
+---------------------------------
+
+Web Interface
+^^^^^^^^^^^^^
+
+#. Navigate to the `Hugging Face website <https://huggingface.co>`_.
+#. Log in to your account.
+#. Click on your profile picture in the top-right corner and select "New dataset."
+#. Follow the on-screen instructions to create a new dataset repository.
+
+Command Line Interface (CLI)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+#. Ensure you have the `huggingface_hub <https://huggingface.co/docs/huggingface_hub/index>`_ library installed.
+#. Use the following Python script to create a new repository:
+
+    .. code-block:: python
+
+        from huggingface_hub import HfApi
+        api = HfApi()
+
+        api.create_repo(repo_id="username/repository_name", repo_type="dataset")
+
+For more information on creating repositories, refer to the `Hugging Face Repositories <https://huggingface.co/docs/hub/repositories>`_.
+
+Uploading Your Dataset
+----------------------
+
+You have two primary methods to upload datasets: through the web interface or using the Python API.
+
+Web Interface
+^^^^^^^^^^^^^
+
+#. Navigate to your dataset repository on the Hugging Face website.
+#. Click on the "Files and versions" tab.
+#. Drag and drop your dataset files into the files section.
+#. Click "Commit changes" to save the files in the repository.
+
+Python API
+^^^^^^^^^^
+
+You can use the following Python script to upload your dataset:
+
+.. code-block:: python
+
+    from huggingface_hub import HfApi
+    api = HfApi()
+
+    api.upload_folder(
+        folder_path="path/to/dataset",
+        repo_id="username/repository_name",
+        repo_type="dataset",
+    )
+
+**Example**:
+
+.. code-block:: python
+
+    from huggingface_hub import HfApi
+    api = HfApi()
+
+    api.upload_folder(
+        folder_path="~/aloha_data/aloha_stationary_block_pickup",
+        repo_id="TrossenRoboticsCommunity/aloha_static_datasets",
+        repo_type="dataset",
+    )
+
+For more information on uploading datasets, refer to the `Hugging Face Uploading <https://huggingface.co/docs/hub/upload>`_.
+
+Downloading Datasets
+--------------------
+
+You can download datasets either by cloning the repository or using the Hugging Face CLI.
+
+Cloning the Repository
+^^^^^^^^^^^^^^^^^^^^^^
+
+To clone the repository, use the following command:
+
+.. code-block:: bash
+
+    $ git clone https://huggingface.co/datasets/username/repository_name
+
+Using the Hugging Face CLI
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+You can also use the Hugging Face CLI to download datasets with the following Python script:
+
+ .. code-block:: python
+
+    from huggingface_hub import snapshot_download
+
+    # Download the dataset
+    snapshot_download(
+        repo_id="username/repository_name",
+        repo_type="dataset",
+        local_dir="path/to/local/directory",
+        allow_patterns="*.hdf5"
+    )
+
+.. note::
+
+   - The dataset episodes are stored in ``.hdf5`` format. Therefore, ensure that you only allow these patterns during download.
+
+For more information on downloading datasets, refer to the `Hugging Face Datasets <https://huggingface.co/docs/hub/download>`_.
+
+Additional Information
+----------------------
+
+- **Repository Management**: Utilize the `Hugging Face Hub documentation <https://huggingface.co/docs/hub/repositories>`_ for detailed instructions on managing repositories, handling versions, and setting permissions.
+- **Dataset Formats**: Hugging Face supports various dataset formats. For this guide, we specifically use the Aloha's native ``.hdf5`` format.
+- **Community Support**: If you encounter any issues, refer to the `Hugging Face community forums <https://discuss.huggingface.co>`_ for additional support.
+
+By following this guide, you should be able to seamlessly upload and download datasets using the Hugging Face platform. For more detailed guides and examples, refer to the `Hugging Face Documentation <https://huggingface.co/docs>`_.

docs/operation/training.rst

@@ -0,0 +1,237 @@
+=======================
+Training and Evaluation
+=======================
+
+Virtual Environment Setup
+=========================
+
+Effective containerization is important when it comes to running machine learning models as there can be conflicting dependencies.
+You can either use a Virtual Environment or Conda.
+
+Virtual Environment Installation and Setup
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+#. Install the virtual environment package:
+
+  .. code-block:: bash
+
+      $ sudo apt-get install python3-venv
+
+#. Create a virtual environment:
+
+  .. code-block:: bash
+
+      $ python3 -m venv ~/act  # Creates a venv "act" in the home directory, can be created anywhere
+
+#. Activate the virtual environment:
+
+  .. code-block:: bash
+
+      $ source act/bin/activate
+
+Conda Setup
+^^^^^^^^^^^
+
+#. Create a virtual environment:
+
+  .. code-block:: bash
+
+    $ conda create -n aloha python=3.8.10
+
+#. Activate the virtual environment:
+
+  .. code-block:: bash
+
+    $ conda activate aloha
+
+Install Dependencies
+^^^^^^^^^^^^^^^^^^^^
+
+Install the necessary dependencies inside your containerized environment:
+
+.. code-block:: bash
+
+    $ pip install dm_control==1.0.14
+    $ pip install einops
+    $ pip install h5py
+    $ pip install ipython
+    $ pip install matplotlib
+    $ pip install mujoco==2.3.7
+    $ pip install opencv-python
+    $ pip install packaging
+    $ pip install pexpect
+    $ pip install pyquaternion
+    $ pip install pyyaml
+    $ pip install rospkg
+    $ pip install torch
+    $ pip install torchvision
+
+Clone Repository
+================
+
+Clone ACT if using Aloha Stationary
+
+.. code-block:: bash
+
+    $ cd ~
+    $ git clone https://github.com/Interbotix/act.git act_training_evaluation
+
+Clone ACT++ if using Aloha Mobile
+
+.. code-block:: bash
+
+    $ cd ~
+    $ git clone https://github.com/Interbotix/act_plus_plus.git act_training_evaluation
+
+Build and Install ACT Models
+============================
+
+.. code-block:: bash
+   :emphasize-lines: 4
+
+    ├── act
+    │   ├── assets
+    │   ├── constants.py
+    │   ├── detr
+    │   ├── ee_sim_env.py
+    │   ├── imitate_episodes.py
+    │   ├── __init__.py
+    │   ├── policy.py
+    │   ├── record_sim_episodes.py
+    │   ├── scripted_policy.py
+    │   ├── sim_env.py
+    │   ├── utils.py
+    │   └── visualize_episodes.py
+    ├── COLCON_IGNORE
+    ├── conda_env.yaml
+    ├── LICENSE
+    └── README.md
+
+
+Navigate to the ``detr`` directory inside the repository and install the detr module whihc contains the model definitions using the below command:
+
+.. code-block:: bash
+
+    $ cd /path/to/act/detr && pip install -e .
+
+Training
+========
+
+To start the training, follow the steps below:
+
+#. Sanity Check: 
+
+    Ensure you have all the hdf5 episodes located in the correct folder after following the data collection steps :ref:`operation/data_collection:Task Creation`.
+
+#. Source ROS Environment:
+
+   .. code-block:: bash
+
+      $ source /opt/ros/humble/setup.bash
+      $ source interbotix_ws/install/setup.bash
+
+#. Activate Virtual Environment:
+
+   .. code-block:: bash
+
+      $ source act/bin/activate
+
+#. Start Training
+
+   .. code-block:: bash
+
+      $ cd /path/to/act/repository/
+      $ python3 imitate_episodes.py \
+        --task_name aloha_stationary_dummy \
+        --ckpt_dir <ckpt dir> \
+        --policy_class ACT \
+        --kl_weight 10 \
+        --chunk_size 100 \
+        --hidden_dim 512 \
+        --batch_size 8 \
+        --dim_feedforward 3200 \
+        --num_epochs 2000 \
+        --lr 1e-5 \
+        --seed 0
+
+.. note::
+
+   - ``task_name`` argument should match one of the task names in the ``TASK_CONFIGS``, as configured in the :ref:`operation/data_collection:Task Creation` section.
+   - ``ckpt_dir``: The relative location where the checkpoints and best policy will be stored.
+   - ``policy_class``: Determines the choice of policy 'ACT'/'CNNMLP'.
+   - ``kl_weight``: Controls the balance between exploration and exploitation.
+   - ``chunk_size``: Determines the length of the action sequence. K=1 is no action chunking and K=episode length is full open loop control.
+   - ``batch_size``: Low batch size leads to better generalization and high batch size results in slower convergence but faster training time.
+   - ``num_epochs``: Too many epochs lead to overfitting; too few epochs may not allow the model to learn.
+   - ``lr``: Higher learning rate can lead to faster convergence but may overshoot the optima, while lower learning rate might lead to slower but stable optimization.
+
+
+.. tip::
+
+  We recommend the following parameters:
+
+  .. list-table::
+    :align: center
+    :widths: 25 75
+    :header-rows: 1
+
+    * - Parameter
+      - Value
+    * - Policy Class
+      - ACT
+    * - KL Weight
+      - 10
+    * - Chunk Size
+      - 100
+    * - Batch Size
+      - 2
+    * - Num of Epochs
+      - 3000
+    * - Learning Rate
+      - 1e-5
+
+Evaluation
+==========
+
+To evaluate a trained model, follow the steps below:
+
+#. Bring up the ALOHA 
+
+   - Stationary: :ref:`operation/stationary:Running ALOHA Bringup`
+   - Mobile: :ref:`operation/mobile:Running ALOHA Bringup`
+
+#. Configure the environment
+
+   .. code-block:: bash
+
+       $ source /opt/ros/humble/setup.bash  # Configure ROS system install environment
+       $ source interbotix_ws/install/setup.bash  # Configure ROS workspace environment
+       $ source /<path_to_aloha_venv>/bin/activate  # Configure ALOHA Python environment
+       $ cd ~/<act_repository>/act/
+
+#. Run the evaluation script
+
+   .. code-block:: bash   
+      :emphasize-lines: 13-14
+
+       python3 imitate_episodes.py \
+        --task_name aloha_stationary_dummy \
+        --ckpt_dir <ckpt dir> \
+        --policy_class ACT \
+        --kl_weight 10 \
+        --chunk_size 100 \
+        --hidden_dim 512 \
+        --batch_size 8 \
+        --dim_feedforward 3200 \
+        --num_epochs 2000 \
+        --lr 1e-5 \
+        --seed 0 \
+        --eval \
+        --temporal_agg
+
+.. note::
+
+   - The ``task_name`` argument should match one of the task names in the ``TASK_CONFIGS``, as configured in the :ref:`operation/data_collection:Task Creation` section.
+   - The ``ckpt_dir`` argument should match the correct relative directory location of the trained policy.
+   - The ``eval`` flag will set the script into evaluation mode.
+   - The ``temporal_agg`` is not required, but helps to smoothen the trajectory of the robots.