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# Gibson Environment for Training Real World AI
You shouldn't play video games all day, so shouldn't your AI. In this project we build a virtual environment that offers real world experience. You can think of it like [The Matrix](https://www.youtube.com/watch?v=3Ep_rnYweaI).
## Note
### Note
This is a 0.1.0 beta release, bug reports are welcome.
### Installation
Table of contents
=================
* [Installation](#installation)
* [Quick Start](#quick-start)
* [Environment Configuration](#environment-configuration)
Installation
=================
The minimal system requirements are the following:
@ -80,19 +88,44 @@ pip install -e baselines
Uninstall gibson is easy, if you installed with docker, just run `docker images -a | grep "gibson" | awk '{print $3}' | xargs docker rmi` to clean up the image. If you installed from source, uninstall with `pip uninstall gibson`
## Demo
Quick Start
=================
After getting into the docker container, you can run a few demos. You might need to run `xhost +local:root` to enable display. If you installed from source, you can run those directly.
```bash
python examples/demo/play_husky_sensor.py ### Use ASWD to control a car to navigate around gates
python examples/demo/play_husky_camera.py ### Use ASWD to control a car to navigate around gates, with camera output
python examples/train/train_husky_navigate_ppo2.py --resolution NORMAL ### Use PPO2 to train a car to navigate down the hall way in gates based on visual input
###More to come!
python examples/train/train_husky_navigate_ppo2.py ### Use PPO2 to train a car to navigate down the hall way in gates based on visual input
```
More examples can be found in `examples/demo` and `examples/train` folder.
Environment Configuration
=================
Each environment is configured with a `yaml` file. Examples of `yaml` files can be found in `examples/configs` folder. Parameters for the file is explained as below (take navigation environment for example):
```
envname: AntClimbEnv # Environment name, make sure it is the same as the class name of the environment
model_id: sRj553CTHiw # Scene id
target_orn: [0, 0, 3.14] # target orientation for navigating, the reference frame is world frame
target_pos: [-7, 2.6, -1.5] # target position for navigating, the reference frame is world frame
initial_orn: [0, 0, 3.14] # initial orientation for navigating
initial_pos: [-7, 2.6, 0.5] # initial position for navigating
fov: 1.57 # field of view for the camera
use_filler: true # use neural network filler or not, it is recommended to leave this argument true
display_ui: true # show pygame ui or not, if in a production environment (training), you need to turn this off
show_dignostic: true # show dignostics overlaying on the RGB image
ui_num: 2 # how many ui components to show
ui_components: [RGB_FILLED, DEPTH] # which are the ui components, choose from [RGB_FILLED, DEPTH, NORMAL, SEMANTICS, RGB_PREFILLED]
output: [nonviz_sensor, rgb_filled, depth] # output of the environment to the robot
resolution: 512 # resolution of rgb/depth image
speed:
timestep: 0.01 # timestep of simulation in seconds
frameskip: 1 # how many frames to run simulation for one action
mode: gui # gui|headless, if in a production environment (training), you need to turn this to headless
verbose: false # show dignostics in terminal
```