Walking Minotaur 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
- University of California (UC) Berkeley,Google Brain
- Organisation type
- Academia,Industry
- Country
- United States of America
- Published
- 19 June 2019
- Authors
- Tuomas Haarnoja, Sehoon Ha, Aurick Zhou, Jie Tan, George Tucker, Sergey Levine
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
- 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
- tokens
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.
- Why it is tracked
- SOTA improvement
- Record confidence
- Unknown
- Citations
- 493
Fig. 3: (a) – (d) Standard benchmark training results. Our method (blue) achieves similar or better performance compared to other algorithms. Note that all other algorithms except ours went through dense hyperparameter tuning to achieve the above learning curves.
Sources
Where this record came from and when it was last checked.
- Reference
- Learning to Walk via Deep Reinforcement Learning
- Last updated
- 25 May 2026
What the numbers mean
About this model
Walking Minotaur robot was published by University of California (UC) Berkeley,Google Brain, in United States of America, in June 2019. It comes out of academia,Industry.
It works in Robotics, and is recorded as doing animal (human/non-human) imitation.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
The reason it appears in this catalogue at all is sOTA improvement.
Answers
Walking Minotaur robot — common questions
Who created Walking Minotaur robot?
Walking Minotaur robot was published by University of California (UC) Berkeley,Google Brain, based in United States of America, categorised as academia,Industry.
When was Walking Minotaur robot released?
Walking Minotaur robot was published in June 2019. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is Walking Minotaur robot used for?
Walking Minotaur robot works in Robotics, and is recorded as handling animal (human/non-human) imitation. 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.
What GPU do I need to run Walking Minotaur robot?
None. Walking Minotaur robot 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.
Is Walking Minotaur robot open source?
No. Walking Minotaur robot has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Walking Minotaur robot have?
No parameter count has been published for Walking Minotaur robot, which is why no memory or speed figure appears on this page.
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.