| # Overview |
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| <p align="center"> |
| <img width="100.0%" src="../images/module_overview.png"> |
| </p> |
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| The **robomimic** framework consists of several modular components that interact to train and evaluate a policy: |
| - **Experiment config**: a config object defines all settings for a training run |
| - **Data**: an hdf5 dataset is loaded into a dataloader, which provides minibatches to the algorithm |
| - **Training**: an algorithm object trains a set of models (including the policy) |
| - **Evaluation**: the policy is evaluated in the environment by conducting a set of rollouts |
| - **Logging**: experiment statistics, model checkpoints, and videos are saved to disk |
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| These modules are encapsulated by the robomimic directory structure: |
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| - `examples`: examples to better understand modular components in the codebase |
| - [`robomimic/algo`](./algorithms.html): policy learning algorithm implementations |
| - [`robomimic/config`](./configs.html): default algorithm configs |
| - [`robomimic/envs`](./environments.html): wrappers for environments, used during evaluation rollouts |
| - `robomimic/exps/templates`: config templates for experiments |
| - [`robomimic/models`](./models.html): network implementations |
| - `robomimic/scripts`: main repository scripts |
| - `robomimic/utils`: a collection of utilities, including the [SequenceDataset](./dataset.html) class to load datasets, and [TensorUtils](../tutorials/tensor_collections.html#tensorutils) to work with nested tensor dictionaries |
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