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"info": {
"author": "Diego Ferigo",
"author_email": "diego.ferigo@iit.it",
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"Intended Audience :: Science/Research",
"License :: OSI Approved :: GNU General Public License v2 or later (GPLv2+)",
"Operating System :: POSIX :: Linux",
"Programming Language :: C++",
"Programming Language :: Python :: 3.6",
"Programming Language :: Python :: 3 :: Only",
"Topic :: Games/Entertainment :: Simulation",
"Topic :: Scientific/Engineering :: Artificial Intelligence"
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gym-ignition
\n\n\n\nWhat\n\u2022\nWhy\n\u2022\nHow\n\u2022\nDemo\n\u2022\nSetup\n\u2022\nCitation\n
\n\n\n\n## What\n\nGym-Ignition is a framework to create **reproducible robotics environments** for reinforcement learning research.\n\nThe project is composed of the following components:\n\n| Component | Description |\n| --------------------------- | ------------------------------------------------------------ |\n| `ScenarI/O` | *Scene Interfaces for Robot Input / Output* is a set of C++ interfaces to abstract the interfacing with simulated and real robots. |\n| `Gazebo ScenarI/O` | Ignition Gazebo bindings that expose the ScenarI/O interfaces. |\n| `gym-ignition` | Python package that provides the tooling to create OpenAI Gym environments for robot learning. |\n| `gym-ignition-environments` | Demo environments created with `gym-ignition` and [gym-ignition-models](https://github.com/dic-iit/gym-ignition-models). |\n| `Gympp` | An _experimental_ C++ port of the OpenAI [Gym interfaces](https://github.com/openai/gym/tree/master/gym), used to create pure C++ environments. |\n\n*Disclaimer: we do not provide support for experimental components.*\n\n## Why\n\nRefer to the [Citation](#citation) for the extended rationale behind this project.\n\n`TL;DR`\n\nWe designed Gym-Ignition driven by the following reasons:\n\n- Advances in RL research are pushed by the development of more complex environments, and vice versa.\n- There's no standard framework in the robotics community for creating simulated robotic environments.\n- Environments that can be transferred from simulation to reality with minimal changes are yet to be seen.\n- Alternative solutions are not developed by roboticists for roboticist, and therefore they do not use familiar tools.\n- Existing robotics environments are typically difficult to adapt to run on different physics engines and different robots.\n- Only few solutions offer realistic rendering capabilities.\n\n## How\n\nThis project interfaces with the new generation of the [Gazebo](http://gazebosim.org) simulator, called [Ignition Gazebo](https://ignitionrobotics.org/libs/gazebo).\nIt is part of the new [Ignition Robotics](http://ignitionrobotics.org) suite developed by [Open Robotics](https://www.openrobotics.org/).\n\nIgnition Robotics is currently under heavy development.\nThough, it already offers enough functionalities that fit this project's aims:\n\n- Simulator-as-a-library\n- New modular architecture\n- C++ utilities developed with a robotic mindset\n- New abstractions of physics engines and rendering engines that exploit runtime plugins\n- Full support of [DART](https://github.com/dartsim/dart) and coming support of [bullet3](https://github.com/bulletphysics/bullet3)\n- Support of distributed simulations\n- Well maintained and packaged\n- [Ignition Fuel](https://app.ignitionrobotics.org/dashboard) database to download models and worlds\n\nOur **Gazebo ScenarI/O** component provides Python bindings of the simulator that are comparable \nto other popular solutions like [pybullet](https://github.com/bulletphysics/bullet3) and [mujoco-py](https://github.com/openai/mujoco-py).\n\n### Features\n\nAt the time of writing, Gym-Ignition offers the following features:\n\n- Environments compatible with [OpenAI Gym](https://github.com/openai/gym/)\n- Worlds and models are [SDF descriptions](http://sdformat.org)\n- Reproducibility guarantees\n- Accelerated and multiprocess execution\n- Environments are a combination of three elements:\n - **Task**: the logic of the decision-making problem. It defines how to process the given action, how to calculate the reward, how to structure the observation, and how to reset the environment. Task objects are robot-independent and runtime-independent.\n - **World**: unified interface to access world resources.\n - **Runtime**: implements the actual step of the environment. Simulated runtimes step the simulator, real-time runtimes deal with real-time execution constraints. A Task object can be executed by any runtime without any change.\n- Experimental support to create C++ environments\n\n## Demo\n\nWe provide a docker image that shows few rollouts executed by a random policy on a cartpole model:\n\n```sh\ndocker pull diegoferigo/gym-ignition:latest\npip3 install rocker\n\n# Intel GPU\nrocker --devices /dev/dri --x11 diegoferigo/gym-ignition /github/examples/python/launch_cartpole.py\n\n# Nvidia GPU\nrocker --x11 --nvidia diegoferigo/gym-ignition /github/examples/python/launch_cartpole.py\n```\n\n## Setup\n\nVisit the [Installation](https://robotology.github.io/gym-ignition/devel/installation/intro.html) section of our website for updated installation instructions.\nMake sure to select the correct branch since the installation could differ depending on the branch you're interested.\n\n## Citation\n\n```\n@INPROCEEDINGS{ferigo2020gymignition,\n title={Gym-Ignition: Reproducible Robotic Simulations for Reinforcement Learning},\n author={D. {Ferigo} and S. {Traversaro} and G. {Metta} and D. {Pucci}},\n booktitle={2020 IEEE/SICE International Symposium on System Integration (SII)},\n year={2020},\n pages={885-890},\n doi={10.1109/SII46433.2020.9025951}\n} \n```\n\n---\n\n**Disclaimer:** Gym-Ignition is an independent project and is not related by any means to OpenAI and Open Robotics.\n\n",
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