Next, we will give an overview of iGibson and briefly explain the different layers of abstraction in our system. In general, the modules from one layer will use and instantiate those from the layer immediately below.


At the bottom layer, we have Dataset and Assets. Dataset contain 3D reconstructed real-world environments. Assets contain models of robots and objects. Download guide can be found here. More info can be found here: Dataset and Assets.

In the next layer, we have Renderer and PhysicsEngine. These are the two pillars that ensure the visual and physics fidelity of iGibson. We developed our own MeshRenderer that supports customizable camera configuration and various image modalities, and renders at a lightening speed. We use the open-sourced PyBullet as our underlying physics engine. It can simulate rigid body collision and joint actuation for robots and articulated objects in an accurate and efficient manner. Since we are using MeshRenderer for rendering and PyBullet for physics simulation, we need to keep them synchronized at all time. Our code have already handled this for you. More info can be found here: Renderer and PhysicsEngine.

In the next layer, we have Scene, Object, Robot, and Simulator. Scene loads 3D scene meshes from gibson2.dataset_path. Object loads interactable objects from gibson2.assets_path. Robot loads robots from gibson2.assets_path. Simulator maintains an instance of Renderer and PhysicsEngine and provides APIs to import Scene, Object and Robot into both of them and keep them synchronized at all time. More info can be found here: Scene, Object, Robot and Simulator.

In the next layer, we have Environment. Environment follows the OpenAI gym convention and provides an API interface for applications such as Algorithms and ROS. Environment usually defines a task for an agent to solve, which includes observation_space, action space, reward, termination condition, etc. More info can be found here: Environment.

In the top and final layer, we have Algorithm and ROS. Algorithm can be any algorithms (from optimal control to model-free reinforcement leanring) that accommodate OpenAI gym interface. We also provide tight integration with ROS that allows for evaluation and visualization of, say, ROS Navigation Stack, in iGibson. More info can be found here: Algorithm and ROS.

We highly recommend you go through each of the Modules below for more details and code examples.