ril-env is an environment management package designed for robotic control, perception and simulation. It provides an API and programmatic interface to manage state-action flow for both real hardware robots and sensors and their simulation counterparts.
Currently, ril-env supports record-replay of the xArm 7 robotic arm on a single machine, with the option of camera perception running in a seperate thread. It also supports recording of the xArm 7 robotic arm on a seperate NUC and visual perception on a main desktop.
More features will be added soon; see the issues tab under the repository.
ril-env is tested on Ubuntu Linux with Python 3.
To get started, first clone the repository.
git clone https://github.com/UCLA-Robot-Intelligence-Lab/ril-env.git
To use this repository with the physical xArm 7 robotic arm, you must use the xArm-Python-Sdk package. Follow the instructions on that repo and install the package at the root of this repository.
Create the conda (recommended) environment. Replace rilenv your preferred name for the environment. Run this at the root of your repository:
conda create -n rilenv python=3.8
conda activate rilenv
conda install --file requirements.txt
Finally, set up the package:
python setup.py
pip install -e .
Ensure that the arm is enabled on the machine you will run the relevant script on.
To run both the perception and xArm 7 robotic arm, you can use the joint_script.py script.
To run perception and the robotic arm seperately, you can either run xarm_script.py and camera_script.py seperately, or to run them simultaneously, you can run the record.sh shell script from the desktop. Currently, the script is hardcoded for a specific NUC and desktop.
You can access nearly all of the parameters for the APIs through their respective config dataclasses. Then, when writing or using a script, you simply need to specify what parameters to change and what to change them to when the config() object is being created.