& a Pytorch implementation of Learning Latent Plans from Play.
This repo is supposed to provide organized & scalable experimentation of data-driven robotics learning. You can adapt it to your own model and environment with minor modifications.
This setup consists of a databse (db/) inspired from [1] storing meta-data of the trajectories collected and a light web-app which renders a video of the trajectory. The DEG module (dataset_env/) provides easy adaption to various environments, dataloaders (deg_base.py), an easy functionality to interact with the DB and store/retrieve trajectories (file_storage.py) - all bundled up. The current implementation includes support for RLBench and (older)Robosuite environments. The collection module (collect_demons/) provides data-collection mechanisms such as teleoperation and imitation policies. Every new model can have it's on directory and the current model/ contains a Pytorch implementation of LfP. The training and testing code are defined in model/ too.
Additional information about each module is provided in their respective READMEs.
Config common to all the modules is defined in global_config.py. Each of the other modules have their own config files (*_config.py) which add to the global config. The config system is designed to automatically change on minor edits (eg. a change in env changes all the paths and other env-related properties).