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