gibson 0.6.5

Real Environment Developed by Stanford University

Github repository

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), and III. embodiment of the agent and making it subject to constraints of space and physics via integrating a physics engine Bulletphysics.

Naming: Gibson environment is named after James 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 Gibson

Paper

Gibson Env: Real-World Perception for Embodied Agents, in CVPR 2018 [Spotlight Oral].

Installation

CUDA Toolkit is necessary to run gibson!

Installing precompiled version from pip

Gibson 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.

pip install gibson

Building from source

If 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:

git clone https://github.com/micheleantonazzi/GibsonEnv.git --recursive
apt-get update
apt-get install doxygen libglew-dev xorg-dev libglu1-mesa-dev libboost-dev \
  mesa-common-dev freeglut3-dev libopenmpi-dev cmake golang libjpeg-turbo8-dev wmctrl \
  xdotool libzmq3-dev zlib1g-dev libsdl-image1.2-dev libsdl-mixer1.2-dev libsdl-ttf2.0-dev \
  libportmidi-dev libfreetype6-dev

Finally install the package using pip (during this process, Gibson is automatically compiled): .. code-block:: bash

pip install -e .

Install required deep learning libraries: Using python3 is recommended. You can create a python3 environment first.