Habitat Lab is a modular high-level library for end-to-end development in embodied AI -- defining embodied AI tasks (e.g. navigation, instruction following, question answering), configuring embodied agents (physical form, sensors, capabilities), training these agents (via imitation or reinforcement learning, or no learning at all as in classical SLAM), and benchmarking their performance on the defined tasks using standard metrics.
Habitat Lab currently uses Habitat-Sim as the core simulator, but is designed with a modular abstraction for the simulator backend to maintain compatibility over multiple simulators. For documentation refer here.
We also have a dev slack channel, please follow this link to get added to the channel. If you want to contribute PRs or face issues with habitat please reach out to us either through github issues or slack channel.