Federation of robots collaboratively train an object manipulation model
This is a quickstart example using LeRobot and Flower that demonstrates how to train a diffusion model collaboratively across 10 individual nodes (each with its own dataset). This example uses the push-t dataset, where the task is to move a letter T object on top of another that is to remain static. The example it's pretty easy to run, and can do so efficiently if you have access to a recent gaming GPU. Although the diffusion model only take 2GB of VRAM (of course you can decide to scale it up), the compute needed to train them isn't negligible. For context, running the example until…
Federation of robots collaboratively train an object manipulation model launched on January 15, 2025 with 7 votes and more than 75% of that year's launches. The site was still online in September 2026, 1 year on.
What it does
Flower: A Friendly Federated AI Framework. Contribute to flwrlabs/flower development by creating an account on GitHub.
This is an introductory example to using 🤗LeRobot with 🌼Flower . It demonstrates that it is feasible to collaboratively train a robotics AI model in remote environments with their local data and then aggregated it in a shared model. In this example, we will federate the training of a Diffusion policy on the PushT dataset. The data will be downloaded and partitioned using Flower Datasets . This example runs best when a GPU is available. Start by cloning the example project. We prepared a single-line command that you can copy into your shell which will checkout the example for you: git clone --depth=1 https://github.com/flwrlabs/flower.git _tmp \ && mv _tmp/examples/quickstart-lerobot . \…from github.com
In the maker’s words, at launch
This is a quickstart example using LeRobot and Flower that demonstrates how to train a diffusion model collaboratively across 10 individual nodes (each with its own dataset). This example uses the push-t dataset, where the task is to move a letter T object on top of another that is to remain static. The example it's…
Does a similar job
all alternatives →- OROK-Robot: open, modular home robot framework for pick-and-drop anywhere2024 · ok-robot.github.io · ▲542
Hi all, excited to share our latest work, OK-Robot, which is an open and modular framework to perform navigation and manipulation with a robot assistant in practically any homes without having to teach the robot anything new! You can simply unbox the target robot, install OK-Robot, give it a "scan" (think a 60 second iPhone video), and start asking the robot to move arbitrary things from A to B. We already tested it out in 10 home environments in New York city, and one environment each in Pittsburgh and Fremont. We based everything off of the current best machine learning models, and so…
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I've had a blast playing with stable diffusion and I see all the potential it will bring to us. I released a service for training your model, just upload 20-30 images and you can have a model of someone or some object doing anything. You can train one model for free a month in a slower queue or you can train many models on a fast queue and with other features for a fee. Here is an example of using it for show product placement: https://app.88stacks.com/image/O6kReClOvrz7 and here is an example of using it for people:…
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I RL-trained an agent that trains models with RL (for ~$1.3k)Jul 2026 · github.com · ▲107RL-training an AI agent to RL-train AI agents. Contribute to Danau5tin/ai-trains-ai development by creating an account on GitHub.
Modular Diffusion – A modular Python library for diffusion models2023 · github.com · ▲40Hello everyone! I've been working on this project for a few months as part of my thesis in Machine Learning. It's meant to be a library that provides an easy-to-use but flexible API to design and train Diffusion Models. I decided to make it because I wanted to quickly prototype a Diffusion Model but there were no good tools to do it with. I think it really can help people prototype their own Diffusion Models a lot faster and only in a few lines of code. The base idea is to have a Model class that takes different modules corresponding to the different aspects of the Diffusion Model process…
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We wanted to do something very challenging to prove to ourselves that we can do anything we put our mind to. The reasoning for why we chose to build a toy TPU specifically is fairly simple: - Building a chip for ML workloads seemed cool - There was no well-documented open source repo for an ML accelerator that performed both inference and training None of us have real professional experience in hardware design, which, in a way, made the TPU even more appealing since we weren't able to estimate exactly how difficult it would be. As we worked on the initial stages of this project, we…
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