Overview

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

These modules are encapsulated by the robomimic directory structure:

examples: examples to better understand modular components in the codebase
robomimic/algo: policy learning algorithm implementations
robomimic/config: default algorithm configs
robomimic/envs: wrappers for environments, used during evaluation rollouts
robomimic/exps/templates: config templates for experiments
robomimic/models: network implementations
robomimic/scripts: main repository scripts
robomimic/utils: a collection of utilities, including the SequenceDataset class to load datasets, and TensorUtils to work with nested tensor dictionaries