Robot Utility Models: open-source robots working in new envs zero shot
Hi all! Sharing our latest work on Robot Utility Models: open source robot models for some useful tasks like door or drawer opening at ~90% success rate that should just work out of the box (given that you have a robot, of course.) Why is this exciting? The state of the art for robot learning thus far has been reliant on pre-training/fine-tuning paradigm: models are first trained on large datasets, then you bring it to your own environment, collect more data, and fine-tune the model on that data. This is not ideal if you want to make robots that work in every home out of the box of…
Robot Utility Models: open-source robots working in new envs zero shot launched on September 9, 2024 with 11 votes, #1,181 of 5,281 launches that month and more than 75% of that year's launches. The site was still online in September 2026, 2 years on.
In plain words
Robot Utility Models provides open-source robot models designed to perform tasks like door and drawer opening with approximately 90% success rate without requiring environment-specific training. Built for roboticists and developers, these models work out-of-the-box across different homes and environments. The approach prioritizes high-quality, diverse training data collected across multiple environments rather than requiring fine-tuning in each new setting, addressing a key limitation in existing robot learning methods.
written from the facts on this page · September 2026
From the sources
Robot Utility Models are trained on a diverse set of environments and objects, and then can be deployed in novel environments with novel objects without any further data or training.
90% success rate in novel environments with 0 additional data or training. Robot models, particularly those trained with large amounts of data, have recently shown a plethora of real-world manipulation and navigation capabilities. Several independent efforts have shown that given sufficient training data in an environment, robot policies can generalize to demonstrated variations in that environment. However, needing to finetune robot models to every new environment stands in stark contrast to models in language or vision that can be deployed zero-shot for open-world problems. In this work, we present Robot Utility Models (RUMs), a framework for training and deploying zero-shot robot…from robotutilitymodels.com
In the maker’s words, at launch
Hi all! Sharing our latest work on Robot Utility Models: open source robot models for some useful tasks like door or drawer opening at ~90% success rate that should just work out of the box (given that you have a robot, of course.) Why is this exciting? The state of the art for robot learning thus far has been…
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 been building Echo (https://echo.tracerml.ai/), an experiment in making one AI system out of a pool of open-weight models rather than choosing a single model and using it for every task. It started with a simple experiment. I took a group of models, including GLM-5.2, Kimi K2.7 and others, and ran them on the same evaluations. Then I measured what would happen if, for each problem, you somehow knew in advance which models would be useful and how their outputs should be combined. That hypothetical system performed substantially better than any individual model in the pool.…
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