deploy: 127c6d3

.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 540d6cd8086c54fb2518e9b9a22b36f2
+config: e8b4ef1a71a4b8841b1ba00d1bb6f1e2
 tags: 645f666f9bcd5a90fca523b33c5a78b7

_sources/operation.rst

@@ -10,3 +10,4 @@ Operation
   ./operation/data_collection.rst
   ./operation/training.rst
   ./operation/hugging_face.rst
+  ./operation/lerobot_guide.rst

_sources/operation/lerobot_guide.rst

@@ -0,0 +1,371 @@
+==========================
+LeRobot X Aloha User Guide
+==========================
+
+Virtual Environment Setup
+=========================
+
+Containerization is crucial for running machine learning models to avoid dependency conflicts.
+You can either use a Virtual Environment (venv) or Conda for this purpose.
+
+Using Virtual Environment (venv)
+--------------------------------
+
+#. Install the virtual environment package:
+
+   .. code-block:: bash
+
+      $ sudo apt-get install python3-venv
+
+#. Create a virtual environment:
+
+   .. code-block:: bash
+
+      $ python3 -m venv ~/lerobot  # Creates a venv "lerobot" in the home directory
+
+#. Activate the virtual environment:
+
+   .. code-block:: bash
+
+      $ source ~/lerobot/bin/activate
+
+Using Conda
+-----------
+
+#. Create a virtual environment:
+
+   .. code-block:: bash
+
+      $ conda create -n lerobot python=3.10
+
+#. Activate the virtual environment:
+
+   .. code-block:: bash
+
+      $ conda activate lerobot
+
+.. note::
+
+   Use either `venv` or `Conda` based on your preference, but **do not** mix them to avoid dependency issues.
+
+Clone Repository
+================
+
+For users of Aloha Stationary:
+
+#. Clone the LeRobot repository:
+
+   .. code-block:: bash
+
+      $ cd ~
+      $ git clone https://github.com/huggingface/lerobot.git
+
+Build and Install LeRobot Models
+================================
+
+#. Build and install the LeRobot models from source:
+
+   .. code-block:: bash
+
+      $ cd lerobot && pip install -e .
+
+Teleoperation
+=============
+
+To teleoperate your robot, follow these steps:
+
+#. Find the serial numbers of your robot's arms and cameras as described in the following documentation:
+
+   - Arm Symlink Setup: `Aloha Post Install Arm Setup <https://docs.trossenrobotics.com/aloha_docs/getting_started/stationary/software_setup.html#arm-symlink-setup>`_
+   - Camera Setup: `Aloha Post Install Camera Setup <https://docs.trossenrobotics.com/aloha_docs/getting_started/stationary/software_setup.html#camera-setup>`_
+
+#. Update the serial numbers in the configuration file: :file:`lerobot/common/configs/robot/aloha.yaml`
+
+#. Run the teleoperation script:
+
+   .. code-block:: bash
+
+      $ python lerobot/scripts/control_robot.py teleoperate \
+         --robot-path lerobot/configs/robot/aloha.yaml
+
+   You will see logs that include information such as delta time (dt), frequency, and read/write times for the robot arms.
+
+#. You can control the teleoperation frequency using the :guilabel:`--fps` argument. For example, to set it to 30 FPS:
+
+   .. code-block:: bash
+
+      $ python lerobot/scripts/control_robot.py teleoperate \
+         --robot-path lerobot/configs/robot/aloha.yaml --fps 30
+
+Customizing Teleoperation with Hydra
+-------------------------------------
+
+You can override the default YAML configurations dynamically using Hydra syntax.
+For example, to change the USB ports of the leader and follower arms:
+
+.. code-block:: bash
+
+   $ python lerobot/scripts/control_robot.py teleoperate \
+      --robot-path lerobot/configs/robot/aloha.yaml \
+      --robot-overrides \
+         leader_arms.main.port=/dev/tty.usbmodem575E0031751 \
+         follower_arms.main.port=/dev/tty.usbmodem575E0032081
+
+
+If you don't have any cameras connected, you can exclude them using Hydra's syntax:
+
+.. code-block:: bash
+
+   $ python lerobot/scripts/control_robot.py teleoperate \
+      --robot-path lerobot/configs/robot/aloha.yaml \
+      --robot-overrides '~cameras'
+
+Recording Data Episodes
+=======================
+
+The system supports episode-based data collection, where episodes are time-bounded sequences of robot actions.
+
+#. Control the recording flow with these arguments:
+
+   - :guilabel:`--warmup-time-s`: Number of seconds for device warmup (default: 10s)
+   - :guilabel:`--episode-time-s`: Number of seconds per episode (default: 60s)
+   - :guilabel:`--reset-time-s`: Time for resetting after each episode (default: 60s)
+   - :guilabel:`--num-episodes`: Number of episodes to record (default: 50)
+
+   Example:
+
+   .. code-block:: bash
+
+      $ python lerobot/scripts/control_robot.py record \
+         --robot-path lerobot/configs/robot/aloha.yaml \
+         --fps 30 \
+         --root data \
+         --repo-id ${HF_USER}/aloha_test \
+         --tags tutorial \
+         --warmup-time-s 5 \
+         --episode-time-s 30 \
+         --reset-time-s 30 \
+         --num-episodes 2
+
+.. note::
+
+   #. The :guilabel:`--num-episodes` defines the total number of episodes to be collected.
+      Therefore it will check the existing output directories for any previously recorded episodes and will start recording from the last recorded episode.
+
+   #. The recorded data is pushed to hugging face hub by default you can set this false by using :guilabel:`--push_to_hub 0`.
+
+.. note::
+
+   #. To push your dataset to Hugging Face's Hub, log in with a write-access token:
+
+      .. code-block:: bash
+
+         $ huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential
+
+   #. Set your Hugging Face username as a variable for ease:
+
+      .. code-block:: bash
+
+         $ HF_USER=$(huggingface-cli whoami | head -n 1)
+
+Visualizing Datasets
+====================
+
+.. video:: ../videos/visualize_dataset.mp4
+   :width: 1280
+   :height: 720
+
+To visualize all the episodes recorded in your dataset, run:
+
+.. code-block:: bash
+
+   $ python lerobot/scripts/visualize_dataset_html.py \
+      --root data \
+      --repo-id ${HF_USER}/aloha_test
+
+To visualize a single dataset episode from the Hugging Face Hub:
+
+.. code-block:: bash
+
+   $ python lerobot/scripts/visualize_dataset.py \
+      --repo-id ${HF_USER}/aloha_static_block_pickup \
+      --episode-index 0
+
+To visualize a single dataset episode stored locally:
+
+.. code-block:: bash
+
+   $ DATA_DIR='./my_local_data_dir' python lerobot/scripts/visualize_dataset.py \
+      --repo-id TrossenRoboticsCommunity/aloha_static_block_pickup \
+      --episode-index 0
+
+Replay Recorded Episodes
+========================
+
+Replaying episodes allows you to test the repeatability of the robot's actions.
+To replay the first episode of your recorded dataset:
+
+.. code-block:: bash
+
+   $ python lerobot/scripts/control_robot.py replay \
+      --robot-path lerobot/configs/robot/aloha.yaml \
+      --fps 30 \
+      --root data \
+      --repo-id ${HF_USER}/aloha_test \
+      --episode 0
+
+.. tip::
+
+   Use different :guilabel:`--fps` values to adjust the frequency of the robot actions.
+
+Training 
+========
+
+To train a policy for controlling your robot, use the following command:
+
+.. code-block:: bash
+
+   $ DATA_DIR=data python lerobot/scripts/train.py \
+      dataset_repo_id=${HF_USER}/aloha_test \
+      policy=act_aloha_real \
+      env=aloha_real \
+      hydra.run.dir=outputs/train/act_aloha_test \
+      hydra.job.name=act_aloha_test \
+      device=cuda \
+      wandb.enable=false
+
+.. note::
+
+   The arguments are explained below:
+
+   #. We provided the dataset with :guilabel:`dataset_repo_id=${HF_USER}/aloha_test`.
+   #. The policy is specified with :guilabel:`policy=act_aloha_real`.
+      This configuration is loaded from :file:`lerobot/configs/policy/act_aloha_real.yaml`.
+   #. The environment is set with :guilabel:`env=aloha_real`.
+      This configuration is loaded from :file:`lerobot/configs/env/aloha_real.yaml`.
+   #. The device is set to :guilabel:`cuda` to utilize an NVIDIA GPU for training.
+   #. :guilabel:`wandb.enable=true` is used for visualizing training plots via [Weights and Biases](https://docs.wandb.ai/quickstart).
+      Ensure you are logged in by running `wandb login`.
+
+Upload Policy Checkpoints
+=========================
+
+Once training is complete, upload the latest checkpoint with:
+
+.. code-block:: bash
+
+   $ huggingface-cli upload ${HF_USER}/act_aloha_test \
+      outputs/train/act_aloha_test/checkpoints/last/pretrained_model
+
+To upload intermediate checkpoints:
+
+.. code-block:: bash
+
+   $ CKPT=010000
+   $ huggingface-cli upload ${HF_USER}/act_aloha_test_${CKPT} \
+      outputs/train/act_aloha_test/checkpoints/${CKPT}/pretrained_model
+
+Evaluation
+==========
+
+To control your robot with the trained policy and record evaluation episodes:
+
+.. code-block:: bash
+
+   $ python lerobot/scripts/control_robot.py record \
+      --robot-path lerobot/configs/robot/aloha.yaml \
+      --fps 30 \
+      --root data \
+      --repo-id ${HF_USER}/eval_aloha_test \
+      --tags tutorial eval \
+      --warmup-time-s 5 \
+      --episode-time-s 30 \
+      --reset-time-s 30 \
+      --num-episodes 10 \
+      -p outputs/train/act_aloha_test/checkpoints/last/pretrained_model
+
+This command is similar to the one used for recording training datasets, with a couple of key changes:
+
+#. The :guilabel:`-p` argument is now included, which specifies the path to your policy checkpoint (e.g., :guilabel:`-p outputs/train/eval_aloha_test/checkpoints/last/pretrained_model`).
+   You can also refer to the model repository on Hugging Face if you have uploaded a model checkpoint there (e.g., :guilabel:`-p ${HF_USER}/act_aloha_test`).
+
+#. The dataset name begins with :guilabel:`eval`, reflecting that you are running inference (e.g., :guilabel:`--repo-id ${HF_USER}/eval_aloha_test`).
+
+You can visualize the evaluation dataset afterward using:
+
+.. code-block:: bash
+
+   $ python lerobot/scripts/visualize_dataset.py \
+      --root data \
+      --repo-id ${HF_USER}/eval_aloha_test
+
+Trossen Robotics Community
+==========================
+
+Pretrained Models
+-----------------
+
+You can download pretrained models from the Trossen Robotics Community on Hugging Face and use them for evaluation purposes.
+To run evaluation on the pretrained models, use the following command:
+
+.. code-block:: bash
+
+   $ python lerobot/scripts/control_robot.py record \
+     --robot-path lerobot/configs/robot/aloha.yaml \
+     --fps 30 \
+     --root data \
+     --repo-id ${HF_USER}/eval_aloha_test \
+     --tags tutorial eval \
+     --warmup-time-s 5 \
+     --episode-time-s 30 \
+     --reset-time-s 30 \
+     --num-episodes 10 \
+     -p ${HF_USER}/act_aloha_test
+
+Datasets for Training and Augmentation
+--------------------------------------
+
+Datasets can also be downloaded from the Trossen Robotics Community on Hugging Face for further training or data augmentation.
+These datasets can be used with your preferred network architectures.
+Instructions for downloading and using these datasets can be found at the following link:
+
+`Dataset Download and Upload Instructions <https://docs.trossenrobotics.com/aloha_docs/operation/hugging_face.html>`_
+
+`Trossen Robotics Community <https://huggingface.co/TrossenRoboticsCommunity>`_
+
+Troubleshooting
+===============
+
+.. warning::
+   If you encounter issues, follow these troubleshooting steps:
+
+#. **OpenCV Installation Issues (Linux)**
+
+   If you encounter OpenCV installation issues, uninstall it via :guilabel:`pip` and reinstall using Conda:
+
+   .. code-block:: bash
+
+      $ pip uninstall opencv-python
+      $ conda install -c conda-forge opencv=4.10.0
+
+#. **FFmpeg Encoding Error (`unknown encoder libsvtav1`)**
+
+   Install FFmpeg with :guilabel:`libsvtav1` support via Conda-Forge or Homebrew:
+
+   .. code-block:: bash
+
+      $ conda install -c conda-forge ffmpeg
+
+   Or:
+
+   .. code-block:: bash
+
+      $ brew install ffmpeg
+
+#. **Arrow Keys Not Working During Data Recording (Linux)**
+
+   Ensure that the :guilabel:`$DISPLAY` environment variable is set correctly.
+
+#. Checkout LeRobot Documentation for further help and details.
+
+   `LeRobot Github <https://github.com/huggingface/lerobot>`_

_static/copybutton.js

@@ -227,7 +227,7 @@ var copyTargetText = (trigger) => {
   let exclude = '.linenos';
 
   let text = filterText(target, exclude);
-  return formatCopyText(text, '$ ', false, true, true, true, '', '')
+  return formatCopyText(text, '$ ', false, true, true, true, '\\', '')
 }
 
   // Initialize with a callback so we can modify the text before copy