Initial commit

examples/aloha_real/README.md

@@ -0,0 +1,73 @@
+# Run Aloha (Real Robot)
+
+This example demonstrates how to run with a real robot using an [ALOHA setup](https://github.com/tonyzhaozh/aloha).
+
+## Prerequisites
+
+This repo uses a fork of the ALOHA repo, with very minor modifications to use Realsense cameras.
+
+1. Follow the [hardware installation instructions](https://github.com/tonyzhaozh/aloha?tab=readme-ov-file#hardware-installation) in the ALOHA repo.
+1. Modify the `third_party/aloha/aloha_scripts/realsense_publisher.py` file to use serial numbers for your cameras.
+
+## With Docker
+
+```bash
+export SERVER_ARGS="--env ALOHA --default_prompt='toast out of toaster'"
+docker compose -f examples/aloha_real/compose.yml up --build
+```
+
+## Without Docker
+
+Terminal window 1:
+
+```bash
+# Create virtual environment
+uv venv --python 3.10 examples/aloha_real/.venv
+source examples/aloha_real/.venv/bin/activate
+uv pip sync examples/aloha_real/requirements.txt
+uv pip install -e packages/openpi-client
+
+# Run the robot
+python examples/aloha_real/main.py
+```
+
+Terminal window 2:
+
+```bash
+roslaunch --wait aloha ros_nodes.launch
+```
+
+Terminal window 3:
+
+```bash
+uv run scripts/serve_policy.py --env ALOHA --default_prompt='toast out of toaster'
+```
+
+## Model Guide
+The Pi0 Base Model is an out-of-the-box model for general tasks. You can find more details in the [technical report](https://www.physicalintelligence.company/download/pi0.pdf).
+
+While we strongly recommend fine-tuning the model to your own data to adapt it to particular tasks, it may be possible to prompt the model to attempt some tasks that were in the pre-training data. For example, below is a video of the model attempting the "toast out of toaster" task.
+
+<p align="center"> 
+  <img src="https://github.com/Physical-Intelligence/openpi/blob/main/examples/aloha_real/toast.gif" alt="toast out of toaster"/> 
+</p>
+
+## Training on your own Aloha dataset
+
+OpenPI suppports training on data collected in the default aloha hdf5 format. To do so you must first convert the data to the huggingface format. We include `scripts/aloha_hd5.py` to help you do this. Once the dataset is converted, add a new `TrainConfig` to `src/openpi/training/configs.py` and replace repo id with the id assigned to your dataset during conversion.
+
+```python
+TrainConfig(
+    name=<your-config-name>,
+    data=LeRobotAlohaDataConfig(
+        repo_id=<your-repo-id>,
+        delta_action_mask=[True] * 6 + [False] + [True] * 6 + [False],
+    ),
+),
+```
+
+Run the training script:
+
+```bash
+uv run scripts/train.py <your-config-name>
+```