Agile Soccer Robot
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
- Organisation type
- Industry
- Country
- United States of America
- Published
- 26 April 2023
- Authors
- Tuomas Haarnoja, Ben Moran, Guy Lever, Sandy H. Huang, Dhruva Tirumala, Markus Wulfmeier, Jan Humplik, Saran Tunyasuvunakool, Noah Y. Siegel, Roland Hafner, Michael Bloesch, Kristian Hartikainen, Arunkumar Byravan, Leonard Hasenclever, Yuval Tassa, Fereshteh Sadeghi, Nathan Batchelor, Federico Casarini, Stefano Saliceti, Charles Game, Neil Sreendra, Kushal Patel, Marlon Gwira, Andrea Huber, Nicole…
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
- Approach
- Reinforcement learning
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
- 3,140,000,000 tokens
". The get-up teacher learns to get up relatively quickly and trained in total for approximately 2.4 · 10^8 environment steps, equivalent to approximately 70 days of simulation time, or 14 hours of wall-clock time. The soccer teacher was trained for 2 · 10^9 environment steps, which took 158 hours of training, equivalent to approximately 580 days of simulated match"
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Wall-clock time
- 240 hours (10 days)
14+158+68 hours: "Training the get-up and soccer teachers took 14 and 158 hours (6.5 days), respectively, and distillation and self-play took 68 hours (see Appendix B for details)"
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
only video demos here https://sites.google.com/view/op3-soccer
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- SOTA improvement
- Record confidence
- Unknown
- Citations
- 262
Likely the best bipedal soccer AI, since it's DeepMind, and related work section just discusses results involving specific soccer skills and quadruped robots: "Whether bipedal or quadrupedal, navigation represents only a fraction of animal and human capabilities. Motivated by this observation, there is a growing interest in whole body control, i.e. tasks in which the whole body is used in flexible ways to interact with the environment. Examples include climbing (Rudin et al., 2022a), getting-up…
Sources
Where this record came from and when it was last checked.
- Reference
- Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
Agile Soccer Robot was published by Google DeepMind, in the country recorded as United States of America, during April 2023. The publishing organisation is categorised as industry.
It works in the domain of Robotics, and is recorded as performing the task of animal (human/non-human) imitation, Sports.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
It was trained on a corpus of about 3,140,000,000 tokens of text.
It is tracked in the underlying dataset for one reason in particular: sOTA improvement.
Answers
Agile Soccer Robot — common questions
Agile Soccer Robot— when was it released?
It was published in April 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.
Agile 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. 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.
Agile 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.
Agile Soccer Robot— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Agile 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.
Agile Soccer Robot— who created it?
It was published by Google DeepMind, based in United States of America, an organisation categorised as industry.
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.