Initial release

.gitignore

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 *.py[cod]
 *$py.class
 
+.DS_Store
+*.html
+
 # C extensions
 *.so
 
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 .installed.cfg
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 MANIFEST
+*.pt
+*.zip
 
 # PyInstaller
 #  Usually these files are written by a python script from a template

README.md

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-# assistive-gym
-Assistive Gym, a physics-based simulation framework for physical human-robot interaction and robotic assistance.
+# Assistive Gym
+
+Assistive Gym is a physics-based simulation framework for physical human-robot interaction and robotic assistance.
+
+Assistive Gym is integrated into the OpenAI Gym interface, enabling the use of existing reinforcement learning and control algorithms to teach robots how to interact with people. 
+
+![Assistive Gym](images/assistive_gym.jpg "Assistive Gym")
+
+### Paper
+A paper on Assistive Gym can be found at https://arxiv.org/abs/
+```
+@article{erickson2019assistivegym,
+  title={Assistive Gym: A Physics Simulation Framework for Assistive Robotics},
+  author={Erickson, Zackory and Gangaram, Vamsee and Kapusta, Ariel and Liu, C. Karen and Kemp, Charles C.},
+  journal={arXiv preprint arXiv:},
+  year={2019}
+}
+```
+
+## Install
+### Simple installation using a python virtual environment.
+We encourage installing Assistive Gym and the custom PyBullet engine in a python virtualenv.  
+A detailed description of each installation step can be found in the [Install Guide](https://github.com/Healthcare-Robotics/assistive-gym/wiki/1.-Install).
+```bash
+python3 -m pip install --user virtualenv
+python3 -m venv env
+source env/bin/activate
+pip3 install git+https://github.com/Zackory/bullet3.git
+git clone https://github.com/Healthcare-Robotics/assistive-gym.git
+cd assistive-gym
+pip3 install .
+# Leave virtual env with: deactivate
+```
+
+## Getting Started
+We provide a [10 Minute Getting Started Guide](https://github.com/Healthcare-Robotics/assistive-gym/wiki/3.-Getting-Started) to help you get familiar with using Assistive Gym for assistive robotics research.
+
+You can visualize the various Assistive Gym environments using the environment viewer.  
+A full list of available environment can be found [Here (Environments)](https://github.com/Healthcare-Robotics/assistive-gym/wiki/2.-Environments).
+```bash
+python3 env_viewer.py --env "FeedingJaco-v0"
+```
+
+We provide pretrained control policies for each robot and assistive task.  
+See [Running Pretrained Policies](https://github.com/Healthcare-Robotics/assistive-gym/wiki/4.-Running-Pretrained-Policies) for details on how to run a pretrained policy.
+
+See [Training New Policies](https://github.com/Healthcare-Robotics/assistive-gym/wiki/5.-Training-New-Policies) for documentation on how to train new control policies for Assistive Gym environments.
+
+Finally, [Creating a New Assistive Environment](https://github.com/Healthcare-Robotics/assistive-gym/wiki/6.-Creating-a-New-Assistive-Environment) discusses the process of creating an Assistive Gym environment for your own human-robot interaction tasks.
+
+## Features
+### Human and robot models 
+Customizable female and male human models (default body sizes and weights matching 50th percentile humans).  
+40 actuated human joints (head, torso, arms, waist, and legs)  
+   
+![Human models](images/human_models.gif "Human models")  
+   
+Four collaborative robots (PR2, Jaco, Baxter, Sawyer).  
+   
+![Robot models](images/robot_models.gif "Robot models")
+### Realistic human joint limits
+Building off of prior research, Assistive Gym provides a model for realistic pose-dependent human joint limits.  
+   
+![Realistic human joint limits](images/realistic_human_joint_limits.gif "Realistic human joint limits")
+### Robot base pose optimization
+A robot's base pose can greatly impact the robot’s ability to physically assist people.  
+We provide a baseline method using joint-limit-weighted kinematic isotopy (JLWKI) to select good base poses near a person.  
+With JLWKI, the robot chooses base poses (position and yaw orientation) with high manipulability near end effector goals.  
+   
+![Robot base pose optimization](images/robot_base_pose_optimization.gif "Robot base pose optimization")
+### Human preferences
+During assistance, a person will typically prefer for the robot not to spill water on them, or apply large forces to their body.  
+Assistive Gym provides a baseline set of human preferences unified across all tasks, which are incorporated directly into the reward function.
+This allows robots to learn to provide assistance that is consist with a person's preferences.  
+   
+![Human preferences](images/human_preferences.gif "Human preferences")
+
+Refer to [the paper](https://arxiv.org/abs/) for details on features in Assistive Gym.