zk0 [zee-ˈkō]

An Open Source humanoid trained collaboratively by a community of builders.

Why

AI technology has advanced enough to speculate that within a decade most people will have their own humanoid buddy. By some estimates humanoids will become $100 Trillion market (5B humanoids * $20,000 per unit).

Today's leading closed source humanoid is trained on 100,000 GPU farm with real world data collected from millions of cars labeled by able human drivers. This is an enormous scale of compute and data that is hard to compete with as a centrazlied entity. However it would be interesting to see if a decentralized approach might produce useful results over time. On the chance that proprietary humanoids ever go rogue, it would be nice to have open source alternatives.

How

zk0 is composed of several major building blocks:

• Generative AI:
  • HuggingFace LeRobot for the Open Source 3D printed robot parts and end-to-end vision language action models.
• Federated Learning:
  • Flower for collaborative training of AI models
• Zero Knowledge Proofs:
  • EZKL for verification of contributed model checkpoints trained on local data.

Quickstart Example

Here is a complete example demonstrating federated learning with the LeRobot PushT dataset. Shows client-server architecture, data partitioning, and model update aggregation.

Contribute

Following is the high level directory structure of the code repository. Jump in, try the example and explore. Contributors are welcome!

zk0
│
├── lerobot             # clone of remote lerobot repo: 
│                       #    https://github.com/huggingface/lerobot.git
│
├── federate            # federated learning layer
│   │
│   └── lerobot_example/
│                       # Federated Learning Example with Flower and LeRobot Diffusion PushT task
│
└── README.md           # This README file

Social Media

It's time for a complete open-source stack for autonomy/robotics plus distributed learning. The first step is here: @LeRobotHF + @flwrlabs LFG 🚀@comma_ai @wayve_ai @Figure_robot @Tesla https://t.co/8O8cSD3SbO https://t.co/oVUOLTvwzm

— nic lane (@niclane7) January 15, 2025

Open-source robots just got a boost. Frameworks like Flower FL enable faster learning, efficient scaling, and continuous knowledge sharing using real-world data. https://t.co/j8VSGiWF0W

— 𝚐𝔪𝟾𝚡𝚡𝟾 (@gm8xx8) January 15, 2025

We are not so far from a future where robots will be constantly learning by interacting with humans and their environments.

Frameworks like @flwrlabs will enable these robots to learn much faster by continuously sharing their learnings.

We really live in a sci-fi movie 😅 https://t.co/kAz3xZ2qvB

— Remi Cadene (@RemiCadene) January 15, 2025

Federated Learning Meets Robotics: 🤖 LeRobot + 🌼 Flower

This demo demonstrates how robots in remote environments can collaboratively train an AI model using their local data, which is then aggregated into a shared model.

In this quickstart, you will train a Diffusion policy… pic.twitter.com/i32MkbxoPW

— Flower (@flwrlabs) January 15, 2025

<iframe width="560" height="315" src="https://www.youtube.com/embed/fwAtTOZttWo?si=3d50oQtSvMvGxNg6" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>

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