MetaMimic
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
- Organisation type
- Industry
- Country
- United States of America
- Published
- 11 October 2018
- Authors
- Tom Le Paine, Sergio Gomez
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Robotics
- Task
- Robotic manipulation
- 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.
- Parameters
- 22M
- Training data
- tokens
"This representational demand motivates the introduction of high-capacity deep neural networks. We found the architecture, shown in Figure 3, with residual connections, 20 convolution layers with 512 channels for a total of 22 million parameters, and instance normalization to drastically improve performance, as shown in Figure 6 of the Experiments section."
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
- Citations
- 26
"By retaining and taking advantage of all its experiences, MetaMimic also substantially outperforms the state-of-the-art D4PG RL agent, when D4PG uses only the current task experiences." I haven't found any standard benchmarks or metrics that they claim SOTA on
Sources
Where this record came from and when it was last checked.
- Reference
- One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL
- Last updated
- 28 November 2025
What the numbers mean
Background
MetaMimic was published by Google, in the country recorded as United States of America, during October 2018. The publishing organisation is categorised as 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.
What went into building it
It is tracked in the underlying dataset for one reason in particular: sOTA improvement.
Answers
MetaMimic — common questions
MetaMimic— what is it used for?
It works in the domain of Robotics, and is recorded as handling the task of robotic manipulation. These are the areas it was designed around; they describe intent rather than a hard boundary.
MetaMimic— 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.
MetaMimic— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
MetaMimic— how many parameters does it have?
It has a parameter count of 22M. "This representational demand motivates the introduction of high-capacity deep neural networks. We found the architecture, shown in Figure 3, with residual connections, 20 convolution layers with 512 channels for a total of 22 million parameters, and instance normalization to drastically improve performance, as shown in Figure 6 of the Experiments section.". 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.
MetaMimic— who created it?
It was published by Google, based in United States of America, an organisation categorised as industry.
MetaMimic— when was it released?
It was published in October 2018. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
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.