.. Getting Started ================ Installation -------------- .. code-block:: bash # Clone the BulletArm Repo git clone https://github.com/ColinKohler/BulletArm.git && cd BulletArm # Install dependencies pip install -r requirements.txt # Install BulletArm either using Pip pip install . # OR by adding it to your PYTHONPATH export PYTHONPATH=/path/to/BulletArm/:$PYTHONPATH Block Stacking Demo -------------------- In order to test your installation, we recommend running the block stacking demo to ensure everything is in working order. .. code-block:: bash python tutorials/block_stacking_demo.py Below we go over the code within the demo and briefly describe the important details. .. code-block:: python :linenos: # The env_factory provides the entry point to BulletArm from bulletarm import env_factory def runDemo(): env_config = {'render': True} # The env_factory creates the desired number of PyBullet simulations to run in # parallel. The task that is created depends on the environment name and the # task config passed as input. env = env_factory.createEnvs(1, 'block_stacking', env_config) # Start the task by resetting the simulation environment. obs = env.reset() done = False while not done: # We get the next action using the planner associated with the block stacking # task and execute it. action = env.getNextAction() obs, reward, done = env.step(action) env.close() Tutorials ------------ We provide a number of `tutorials `_ including an `introcutory tutorial `_ demonstrating how to collect data for training of a RL agent. Examples on how to extend PyBullet for either `creating new tasks `_ or `creating new robots `_ are also included.