Simulate’s API is inspired by the great Kubric’s API. The user create a scene and add assets in it (objects, cameras, lights if needed). Once the scene is created you can save/share it and also render or do simulations using one of the backend rendering/simulation engines (at the moment Unity, Blender and Godot). The saving/sharing format is engine agnostic and using the industry standard glTF format for saving scenes.

Create a virtual env and then install the code style/quality tools as well as the code base locally

pip install --upgrade simulate

Project Structure

The Python API is located in src/simulate. It allows creation and loading of scenes, and sending commands to the backend.

The backend, currently just Unity, is located in integrations/Unity. This is currently a Unity editor project, which must be opened in Unity 2021.3.2f1. In the future, this will be built as an executable, and spawned by the Python API.

Loading a scene from the hub or a local file

Loading a scene from a local file or the hub is done with Scene.create_from(), saving or pushing to the hub with scene.save() or scene.push_to_hub():

from simulate import Scene

scene = Scene.create_from('tests/test_assets/fixtures/Box.gltf')  # either local (priority) or on the hub with full path to file
scene = Scene.create_from('simulate-tests/Box/glTF/Box.gltf', is_local=False)  # Set priority to the hub file

scene.save('local_dir/file.gltf')  # Save to a local file
scene.push_to_hub('simulate-tests/Debug/glTF/Box.gltf')  # Save to the hub

Creating a Scene and adding/managing Objects in the scene

import simulate as sm

scene = sm.Scene()
scene += sm.Plane() + sm.Sphere(position=[0, 1, 0], radius=0.2)

>>> scene
>>> Scene(dimensionality=3, engine='PyVistaEngine')
>>> └── plane_01 (Plane - Mesh: 121 points, 100 cells)
>>>     └── sphere_02 (Sphere - Mesh: 842 points, 870 cells)

scene.show()

An object (as well as the Scene) is just a node in a tree provided with optional mesh (as pyvista.PolyData structure) and material and/or light, camera, agents special objects.

python examples/basic/objects.py

Objects are organized in a tree structure

# Add two copy of the sphere to the scene as children of the root node (using list will add all objects on the same level)
# Using `.copy()` will create a copy of an object (the copy doesn't have any parent or children)
scene += [scene.plane_01.sphere_02.copy(), scene.plane_01.sphere_02.copy()]

>>> scene
>>> Scene(dimensionality=3, engine='pyvista')
>>> ├── plane_01 (Plane - Mesh: 121 points, 100 cells)
>>> │   └── sphere_02 (Sphere - Mesh: 842 points, 870 cells)
>>> ├── sphere_03 (Sphere - Mesh: 842 points, 870 cells)
>>> └── sphere_04 (Sphere - Mesh: 842 points, 870 cells)

# Remove the last added sphere
>>> scene.remove(scene.sphere_04)
>>> Scene(dimensionality=3, engine='pyvista')
>>> ├── plane_01 (Plane - Mesh: 121 points, 100 cells)
>>> │   └── sphere_02 (Sphere - Mesh: 842 points, 870 cells)
>>> └── sphere_03 (Sphere - Mesh: 842 points, 870 cells)

Objects can be translated, rotated, scaled

Let’s scale the second sphere uniformly

Inspect the current position and scaling values

We can also translate from a vector and rotate from a quaternion or along the various axis


<h3 id="visualization-engine">Visualization engine</h3>

A default vizualization engine is provided with the vtk backend of `pyvista`.

Starting the vizualization engine can be done simply with `.show()`.


You can find bridges to other rendering/simulation engines in the `integrations` directory.

<h1 id="reinforcement-learning-rl-with-simulate">Reinforcement Learning (RL) with Simulate</h1>

Simulate is designed to provide easy and scalable integration with reinforcement learning algorithms.
The core abstraction is through the [RLEnv](/docs/simulate/v0.0.1/en/api/rl_env#simulate.RLEnv) class that wraps a `Scene`.
The [RLEnv](/docs/simulate/v0.0.1/en/api/rl_env#simulate.RLEnv) allows an [Actuator](/docs/simulate/v0.0.1/en/api/actuators#simulate.Actuator) to be manipulated by an external agent or policy.

It is core to the design of Simulate that we are *not creating* Agents, but rather providing an interface for applications of machine learning and embodied AI.
The core API for RL applications can be seen below, where Simulate constrains the information that flows from the Scene to the external agent through an Actuator abstraction.

<p align="center">
    <br>
    <img src="https://user-images.githubusercontent.com/10695622/192663853-a7543091-8d45-4fba-b8dc-2b632d66a35f.png" width="500"/>
    <br>
</p>

At release, we include a set of pre-designed `Actor`'s that can act or navigate a scene. An `Actor` inherits from an `Object3D` and has sensors, actuators, and action mappings.


<h3 id="tips">Tips</h3>

If you are running on GCP, remember to not install `pyvistaqt`, and if you did so, uninstall it in your environment, since QT doesn't work well on GCP.

Quicktour