Soccer Robot

Closed weights Google DeepMind,University College London (UCL) May 2024

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,University College London (UCL)
Organisation type
Industry,Academia
Country
United States of America, United Kingdom of Great Britain and Northern Ireland
Published
3 May 2024
Authors
Dhruva Tirumala, Markus Wulfmeier, Ben Moran, Sandy Huang, Jan Humplik, Guy Lever, Tuomas Haarnoja, Leonard Hasenclever, Arunkumar Byravan, Nathan Batchelor, Neil Sreendra, Kushal Patel, Marlon Gwira, Francesco Nori, Martin Riedmiller, Nicolas Heess

What it does

The problem areas the model was built for. A model can carry several of each.

Domain
Robotics
Task
Animal (human/non-human) imitation, Sports

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.

Training data
tokens

The training run

What it physically took to train: which chips, how many, for how long, and what that drew from the wall.

Training hardware
NVIDIA V100,Google TPU v2
Chips used
64

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

How it is classified

Labels the source dataset applies when tracking notable models, and how confident it is in the entry.

Record confidence
Unknown

Sources

Where this record came from and when it was last checked.

Reference
Learning Robot Soccer from Egocentric Vision with Deep Reinforcement Learning
Last updated
28 November 2025

What the numbers mean

Background

Soccer Robot was published by Google DeepMind,University College London (UCL), in the country recorded as United States of America, during May 2024. The category the publisher falls under is industry,Academia.

It works in the domain of Robotics, and is recorded as performing the task of animal (human/non-human) imitation, Sports.

This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.

Answers

Soccer Robot — common questions

01

Soccer Robot— is it open source?

No. Its weights have not been published, so it exists only as a service controlled by its owner.

02

Soccer Robot— how many parameters does it have?

No parameter count has been published for it, which is why no memory or speed figure appears on this page.

03

Soccer Robot— who created it?

It was published by Google DeepMind,University College London (UCL), based in United States of America, an organisation categorised as industry,Academia.

04

Soccer Robot— when was it released?

It was published in May 2024. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.

05

Soccer Robot— what is it used for?

It works in the domain of Robotics, and is recorded as handling the task of animal (human/non-human) imitation, Sports. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.

06

Soccer Robot— 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.

Source

Original publication

Record last updated 28 November 2025

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.