Package: gibson
The source code is available on thisGithub repository.This package is generated starting from GibsonEnv project.
You can find the original source codehereor you can visit theofficial website.Summary: Perception and being active (i.e. having a certain level of motion freedom) are closely tied. Learning active perception and sensorimotor control in the physical world is cumbersome as existing algorithms are too slow to efficiently learn in real-time and robots are fragile and costly. This has given a fruitful rise to learning in the simulation which consequently casts a question on transferring to real-world. We developed Gibson environment with the following primary characteristics:I.being from the real-world and reflecting its semantic complexity through virtualizing real spaces,II.having a baked-in mechanism for transferring to real-world (Goggles function), andIII.embodiment of the agent and making it subject to constraints of space and physics via integrating a physics engineBulletphysics.Naming: Gibson environment is named afterJames J. Gibson, the author of “Ecological Approach to Visual Perception”, 1979. “We must perceive in order to move, but we must also move in order to perceive” – JJ GibsonPaperGibson Env: Real-World Perception for Embodied Agents, in CVPR 2018 [Spotlight Oral].InstallationCUDA Toolkit is necessary to run gibson!Installing precompiled version from pipGibson can be simply installed from pip. The pip version of Gibson is precompiled only for linux machines. If you use another SO, you have to recompile Gibson from source.sudoaptinstalllibopenmpi-devpipinstallgibsonBuilding from sourceIf you don’t want to use the precompiled version, you can also install gibson locally. This will require some dependencies to be installed.First, make sure you have Nvidia driver and CUDA installed. If you install from source, CUDA 9 is not necessary, as that is for nvidia-docker 2.0.
Then, clone this repository recursively to download the submodules  and install the following dependencies:gitclonehttps://github.com/micheleantonazzi/GibsonEnv.git--recursiveapt-getupdateapt-getinstalldoxygenlibglew-devxorg-devlibglu1-mesa-devlibboost-dev\mesa-common-devfreeglut3-devlibopenmpi-devcmakegolanglibjpeg-turbo8-devwmctrl\xdotoollibzmq3-devzlib1g-devlibsdl-image1.2-devlibsdl-mixer1.2-devlibsdl-ttf2.0-dev\libportmidi-devlibfreetype6-devFinally install the package using pip (during this process, Gibson is automatically compiled):pipinstall-e.Install required deep learning libraries: Using python3 is recommended. You can create a python3 environment first.Download Gibson assetsAfter the installation of Gibson, you have to set up the assets data (agent models, environments, etc).
The folder that stores the necessary data to run Gibson environment must be set by the user.
To do this, simply run this commandgibson-set-assets-pathin a terminal and then follow the printed instructions.
This script asks you to insert the path where to save the Gibson assets.
Inside this folder, you have to copy the environmentcore assets data(~= 300MB)
and theenvironments dataset(~= 10GB).
The environment data must be located inside a sub-directory calleddataset.
You can add more environments by adding them inside thedatasetfolder located in the previously set path.
Users can download and copy manually these data inside the correct path or they can use dedicated python utilities.
To easily download Gibson assets, typing in a terminal:gibson-set-assets-path# This command allows you to set the default Gibson assets foldergibson-download-assets-coregibson-download-dataset