Robo-Daycare

Humans are capable of learning new tasks very quickly by leveraging their experience. Our innate intelligence allows us to recognize objects from very few examples and learnto complete a new foreign task with very little experience. Our initial goal is to teach a robot how to walk using the Proximal Policy Optimization algorithm and then use it as a baseline for various other transfer learning tasks like playing soccer. During the course of this, we found that transfer learning is a reliable method to layer behaviors on top of each other in a way that would be necessary for complex tasks like playing soccer.

Robo-Daycare Website

Setting Up

First, you can perform a minimal installation of OpenAI Gym with

git clone https://github.com/openai/gym.git
cd gym
pip install -e .

Then, Pybullet-Gym is to be installed. Clone the repository and install locally

git clone https://github.com/benelot/pybullet-gym.git
cd pybullet-gym
pip install -e .

Important Note: Do not use python setup.py install as this will not copy the assets (you might get missing SDF file errors).

To test installation, open python and run

import gym  # open ai gym
import pybulletgym  # register PyBullet enviroments with open ai gym

env = gym.make('HumanoidPyBulletEnv-v0')
# env.render() # call this before env.reset, if you want a window showing the environment
env.reset()  # should return a state vector if everything worked

To train, run:

python3 PPO_continuous.py

To visualize with the weights generated from training:

python3 test_continuous.py

State of implementations

Environment Name
New Environments
Walker2DWindPyBulletEnv-v0
HopperWindPyBulletEnv-v0
HumanoidWindPyBulletEnv-v0
Walker2DDribblePyBulletEnv-v0
Walker2DKickPyBulletEnv-v0
Walker2DSoccerPyBulletEnv-v0