RoboCat
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Google DeepMind,Google
- Organisation type
- Industry,Industry
- Country
- United States of America
- Published
- 20 June 2023
- Authors
- Konstantinos Bousmalis, Giulia Vezzani, Dushyant Rao, Coline Devin, Alex X. Lee, Maria Bauza, Todor Davchev, Yuxiang Zhou, Agrim Gupta, Akhil Raju, Antoine Laurens, Claudio Fantacci, Valentin Dalibard, Martina Zambelli, Murilo Martins, Rugile Pevceviciute, Michiel Blokzijl, Misha Denil, Nathan Batchelor, Thomas Lampe, Emilio Parisotto, Konrad Żołna, Scott Reed, Sergio Gómez Colmenarejo, Jon Scholz…
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Robotics
- Task
- Robotic manipulation
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Parameters
- 1.2B
- Training data
- tokens
"Most of the experimental results are based on models with a 1.18B-parameter decoder-only transformer (Vaswani et al., 2017) with 24 layers, an embedding size of 2048, and a post-attention feedforward hidden size of 8196." page 8
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Unreleased
https://deepmind.google/discover/blog/robocat-a-self-improving-robotic-agent/
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Speculative
- Citations
- 96
Sources
Where this record came from and when it was last checked.
- Reference
- RoboCat: A Self-Improving Foundation Agent for Robotic Manipulation
- Last updated
- 25 May 2026
What the numbers mean
Background
RoboCat was published by Google DeepMind,Google, in the country recorded as United States of America, during June 2023. It comes out of an organisation categorised as industry,Industry.
It works in the domain of Robotics, and is recorded as performing the task of robotic manipulation.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
RoboCat — common questions
RoboCat— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
RoboCat— how many parameters does it have?
It has a parameter count of 1.2B. "Most of the experimental results are based on models with a 1.18B-parameter decoder-only transformer (Vaswani et al., 2017) with 24 layers, an embedding size of 2048, and a post-attention feedforward hidden size of 8196." page 8. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
RoboCat— who created it?
It was published by Google DeepMind,Google, based in United States of America, an organisation categorised as industry,Industry.
RoboCat— when was it released?
It was published in June 2023. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
RoboCat— what is it used for?
It works in the domain of Robotics, and is recorded as handling the task of robotic manipulation. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
RoboCat— what GPU do I need to run it?
None. This is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.