The field of reinforcement learning is growing exponentially, but cutting-edge research requires time, money, and infrastructure. This lack of accessibility in research is slowing progress in the field, and makes reproducing or comparing results almost impossible.
To tackle this problem, we're excited to introduce ROBEL (RObotics BEnchmarks for Learning): a collection of affordable, reliable hardware designs for studying dexterous manipulation and locomotion on real-world hardware. With simple assembly instructions, detailed simulations, and all open-sourced software, we hope to open up the field of Reinforcement Learning to everyone and accelerate progress around the world.
Features
Gym Compliant -- ROBEL environments are fully Gym-compliant and can be used with any reinforcement learning library that interfaces with Gym environments.
Simulated backends -- ROBEL also includes simulated equivalents of the introduced benchmarks to facilitate prototyping and debugging needs. Simulation backend is provided by MuJoCo. Support for Bullet is planned.
Hardware interface -- Communication with hardware is done through the DynamixelSDK.
External tracking support -- For D'Kitty environments, external tracking is supported through OpenVR tracking.
Open-source design -- The hardware design and build instructions are fully open-sourced and are available for anyone to build their own robots.
Citation [ paper(arxiv) ]
@INPROCEEDINGS{Kumar_ROBEL,
AUTHOR = {Michael Ahn AND Henry Zhu AND Kristian Hartikainen AND Hugo Ponte AND Abhishek Gupta AND Sergey Levine AND Vikash Kumar},
TITLE = "{ROBEL: RObotics BEnchmarks for Learning with low-cost robots}",
BOOKTITLE = {Conference on Robot Learning (CoRL)},
YEAR = {2019}, }