Why gym-ignition¶

In the previous sections we described why we developed ScenarIO and why we used Ignition Gazebo to implement its simulation back-end. While we mentioned few advantages for the robot learning domain, ScenarIO remains a general C++ library that can be used for generic robotic applications.

The reinforcement learning community, in the past years, converged towards Python for the development of the environments containing the decision-making logic. OpenAI Gym became the reference interface to provide a clear separation between agents and environments. A widespread interface is powerful because if you implement an environment that exposes the gym.Env interface, you can then use all the countless frameworks provided by the community to train a policy selecting your favourite algorithm.

The Python package gym_ignition enables you to create robotic environments compatible with OpenAI Gym. Being based on ScenarIO, it enables to develop environments that can run not only on different physics engines, but also on real robots.

You can think of gym_ignition as a way to help you structuring your environment. If you know how pytorch-lightning relates to PyTorch, the same applies to the interaction between gym-ignition and ScenarIO. Thanks to the Task and Runtime interfaces, gym_ignition abstracts away all the unnecessary boilerplate that otherwise you have to copy and paste between environments.

For example, GazeboRuntime provides all boilerplate code to take your implementation of a Task and simulate it with Ignition Gazebo.

Furthermore, we provide useful classes with functionalities that are commonly required by robotic environments, like Inverse Kinematic (InverseKinematicsNLP) and multibody dynamics algorithms (KinDynComputations) with full support of floating-base systems.

Note

Developing environments for robot learning in C++ is a valid choice and the community has shown different examples. ScenarIO can be used to develop C++ environments as well, however we find more useful using Python since it allows a faster prototyping.

Note

To the best of our knowledge, the first package that implemented a structure that abstracts the task, the robot, and the simulator is openai_ros. We have been inspired by its structure in the early stage of development, and the current interfaces implemented in gym-ignition are an evolution of the original architecture.