Huggingface Lerobot: Revolutionizing AI-Robotics with Open-Source Code

Huggingface Lerobot is a game-changer in the world of AI-robotics, and it's all thanks to its open-source code. This means that developers from all over the world can access and contribute to the code, accelerating innovation and progress.

Lerobot's open-source code is built on top of the popular Transformers library, which provides a wide range of pre-trained models and tools for natural language processing and computer vision tasks. This foundation enables Lerobot to tackle complex tasks with ease.

One of the key features of Lerobot is its ability to learn from human feedback, allowing it to adapt and improve over time. This is made possible by its integration with the Hugging Face Dataset platform, which provides a vast collection of labeled datasets for training and fine-tuning models.

Worth a look: How to Use Models from Huggingface

LeRobot is a robust framework that serves as a "Transformers for robotics." It's not just a software package, but a comprehensive platform that includes a versatile library for sharing, visualizing data, and training state-of-the-art models.

Users can access a plethora of pre-trained models to jumpstart their projects, which is super helpful for those who want to get started quickly.

LeRobot integrates seamlessly with physics simulators, allowing users to simulate and test their AI models in a virtual environment without needing physical robotics hardware.

This means that enthusiasts and developers can experiment and refine their ideas without the need for costly hardware or physical space.

On a similar theme: How to Use Huggingface Models in Python

Hugging Face Llama is a large language model that can be fine-tuned for a wide range of tasks. It's built on top of the Llama model, which has 13 billion parameters.

To get started with Hugging Face Llama, you'll first need to install the Transformers library, which is the foundation of Hugging Face's model ecosystem. This library provides a simple interface to interact with pre-trained models like Llama.

Once you have the library installed, you can use the Hugging Face CLI to fine-tune Llama for your specific task.

Suggestion: Llama 2 Fine Tuning Huggingface

Cloning a repository is a great way to create a copy of your existing project, which can be useful for testing or collaborating with others. This process is called cloning because it creates a duplicate of your repository.

To clone a repository, you'll need to use a Git client, such as Git Bash or the GitHub Desktop app. You can find the clone URL in the repository's settings or by clicking the "Clone or download" button.

The clone command will create a new directory with the same name as your repository. The new directory will contain all the files and folders from the original repository.

Additional reading: Huggingface Git

The LeRobot project is creating the largest crowdsourced robotics dataset ever attempted, involving collaboration with universities, startups, tech firms, and individual hobbyists.

This massive dataset includes terabytes of onboard video recordings, which are being formatted using the lightweight LeRobotDataset for easy upload and download via the Hugging Face hub.

Hugging Face is fostering an inclusive environment by lowering barriers to entry and promoting shared knowledge and resources.

By doing so, they aim to cultivate a community that could redefine the landscape of AI robotics.

You can use the koch.yaml file and our teleoperate function to control your robot, which is a more efficient way than manually running the python code in a terminal window.

To do this, you'll need to provide the path to the robot yaml file, such as lerobot/configs/robot/koch.yaml, and use the control_robot.py script to instantiate your robot.

You'll see a lot of lines appearing in the terminal, like the date and time of the call to the print function, the end of the file name and the line number where the print function is called, the delta time, and the frequency.

Here's a breakdown of the information you'll see:

You can also override any entry in the yaml file using the --robot-overrides and the hydra.cc syntax, and remove cameras dynamically using the same syntax.

The LeRobot library on Github offers a wide range of robotic capabilities, from simple robotic arms used in education and research to sophisticated humanoids.

These capabilities enable the toolkit to adapt and control any form of robot, providing versatility and scalability in robotics applications.

The code can be trained on real-world datasets, such as the Aloha project, and can even be used to train robots to navigate unmapped spaces and grasp objects from video.

This versatility is made possible by the toolkit's ability to handle a range of robotic hardware, from simple to complex systems.

The LeRobot codebase has been validated by replicating state-of-the-art results in simulations, including the famous ACT policy, which has been retrained and made available as a pretrained checkpoint.

You can use koch.yaml and our teleoperate function to control your robot, which is a more efficient way than manually running the python code in a terminal window.

To do this, you'll need to use the control_robot.py script from lerobot/scripts and provide the path to the robot yaml file, for example lerobot/configs/robot/koch.yaml.

This will give you a lot of information about the performance of your robot, including the date and time of each call to the print function, the line number where the function is called, and the frequency of the calls.

Here are some key performance metrics you'll see:

You can also control the maximum frequency by adding the fps argument, for example --fps 30.

Note that you can override any entry in the yaml file using the --robot-overrides and hydra.cc syntax.

The decision to make LeRobot open-source is a strategic one, aimed at avoiding the concentration of power and innovation within a handful of corporations. This approach allows for a global community of developers, researchers, and hobbyists to contribute to and benefit from the collective advancement of AI robotics.

By making LeRobot open-source, Hugging Face invites a global community to contribute to its development. This collective effort can lead to faster innovation and wider adoption of AI robotics.

A fresh viewpoint: Open Webui Add Tools from Huggingface

Open-source philosophy encourages collaboration and knowledge sharing, which can lead to breakthroughs that might not have been possible within a single corporation. This approach also fosters a sense of community and shared ownership among contributors.

The open-source nature of LeRobot allows developers to access its source code, modify it, and distribute their own versions. This flexibility is a key aspect of open-source philosophy.

To train a policy for your robot, use the python lerobot/scripts/train.py script. This script requires a few arguments, including the dataset, policy, environment, and device.

The dataset is specified with dataset_repo_id, which can be a local directory or a repository ID on Hugging Face hub. You can also use the DATA_DIR argument to access your dataset stored in your local data directory.

The policy is specified with policy, which loads configurations from a YAML file. For example, policy=act_koch_real loads configurations from lerobot/configs/policy/act_koch_real.yaml. This policy uses 2 cameras as input laptop and phone.

Explore further: Hugging Face Local

The environment is set with env, which loads configurations from a YAML file. For example, env=koch_real loads configurations from lerobot/configs/env/koch_real.yaml. This configuration should match your dataset and robot.

You can also use wandb.enable=true to visualize training plots via Weights and Biases, but make sure you're logged in by running wandb login.

Here are the required arguments for the train.py script:

Additionally, you can build and install the LeRobot models from source by running the following commands:

Training is a crucial step in deploying your robot, and it's actually quite straightforward. To train a policy for controlling your robot, you'll need to use a Python script, specifically the `train.py` script.

You'll need to provide a few arguments to this script, including the dataset, policy, environment, device, and whether to use Weights and Biases for visualizing training plots. The dataset should be specified with `dataset_repo_id`, which can be a link to your dataset on Hugging Face hub.

If this caught your attention, see: Huggingface Training Service

The policy is specified with `policy`, and this configuration is loaded from a YAML file. For example, if you're using the `act_koch_real` policy, it will load configurations from `lerobot/configs/policy/act_koch_real.yaml`. This policy uses 2 cameras as input, so make sure to update the YAML file if your dataset has different cameras.

The environment is set with `env`, and this configuration is loaded from another YAML file. For instance, if you're using the `koch_real` environment, it will load configurations from `lerobot/configs/env/koch_real.yaml`, which should match your dataset and robot.

You'll also need to specify the device you're using for training. If you have an NVIDIA GPU, you can use `device=cuda`. If you're using Apple silicon, you can use `device=mps`. And if you want to use Weights and Biases for visualizing training plots, you can set `wandb.enable=true`, but make sure you're logged in by running `wandb login`.

Here's a summary of the arguments you'll need to provide:

By following these steps, you'll be able to train a policy for controlling your robot and get it ready for deployment.

To evaluate your robot's performance, you'll need to control it with the trained policy and record evaluation episodes. This process is similar to recording training datasets, but with a few key changes.

You'll need to specify the path to your policy checkpoint using the -p argument. For example, you can use -p outputs/train/eval_aloha_test/checkpoints/last/pretrained_model or refer to the model repository on Hugging Face using -p ${HF_USER}/act_aloha_test.

The dataset name should begin with eval, indicating that you're running inference. This means you'll use a name like –repo-id ${HF_USER}/eval_aloha_test.

After recording the evaluation dataset, you can visualize it using a command that we'll cover in the next section.

First, you need to build and install the LeRobot models from source. This is done by navigating to the LeRobot directory and running the command `pip install -e`.

To get the LeRobot models running, you'll need to install dependencies for Intel RealSense cameras and Dynamixel servos. This can be achieved by running the command `pip install [intelrealsense, dynamixel]` from within the LeRobot directory.

Curious to learn more? Check out: Ollama Huggingface Models

To summarize, the steps for model deployment are:

By following these steps, you'll be able to successfully deploy the LeRobot models and start working with real robots.