  The domain of Embodied AI, in which agents learn to complete tasks through
interaction with their environment from egocentric observations, has
experienced substantial growth with the advent of deep reinforcement learning
and increased interest from the computer vision, NLP, and robotics communities.
This growth has been facilitated by the creation of a large number of simulated
environments (such as AI2-THOR, Habitat and CARLA), tasks (like point
navigation, instruction following, and embodied question answering), and
associated leaderboards. While this diversity has been beneficial and organic,
it has also fragmented the community: a huge amount of effort is required to do
something as simple as taking a model trained in one environment and testing it
in another. This discourages good science. We introduce AllenAct, a modular and
flexible learning framework designed with a focus on the unique requirements of
Embodied AI research. AllenAct provides first-class support for a growing
collection of embodied environments, tasks and algorithms, provides
reproductions of state-of-the-art models and includes extensive documentation,
tutorials, start-up code, and pre-trained models. We hope that our framework
makes Embodied AI more accessible and encourages new researchers to join this
exciting area. The framework can be accessed at: https://allenact.org/
