ROBEL is an open-source platform of cost-effective robots and associated reinforcement learning environments for benchmarking reinforcement learning in the real world. It provides Gym-compliant environments that easily run in both simulation (for rapid prototyping) and on real hardware. ROBEL robots are robust and scalable - they have facilitated over 14000 hours (as of Aug'19) of real-world training with various learning-based methods. Benchmarks using several learning-based methods are provided for ease of comparison and extensibility. Refer to ROBEL's webpage for full details.
 |
|---|
| ROBEL robots: D’Kitty (left) and D’Claw (middle and right) |
ROBEL introduces two robots that are modular, extensible, easy to build, and are lower cost compared to many existing robotics research platforms:
- D'Claw is a nine degree of freedom manipulation platform capable of performing dexterous manipulation.
- D'Kitty is a twelve degree of freedom quadruped capable of agile locomotion.
- 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.
- Hardware interface -- ROBEL is built using Dynamixel motors and communicates with the hardware device 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.
Please refer to the Hardware Guide for getting started with the ROBEL hardware platforms.
Download MuJoCo Pro 2.00 from the
MuJoCo website. You should extract this
to ~/.mujoco/mujoco200. Ensure your MuJoCo license key is placed at
~/.mujoco/mjkey.txt.
Add the following line to your ~/.bashrc (or equivalent) in order for
mujoco_py to install properly:
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$HOME/.mujoco/mujoco200/binRun source ~/.bashrc afterwards.
ROBEL requires Python 3.5 or higher. You can install ROBEL by running:
pip install robelWe recommend doing this in a virtualenv or a Conda environment to avoid
interfering with system dependencies or existing packages.
Alternatively, you can install directly from the repository by running:
git clone --recurse-submodules https://github.com/google-research/robel.git
pip install -e robel/To run on hardware, additionally install the DynamixelSDK Python API:
pip install git+https://github.com/ROBOTIS-GIT/DynamixelSDK.git#subdirectory=pythonimport robel
import gym
# Create a simulation environment for the D'Claw turn task.
env = gym.make('DClawTurnFixed-v0')
# Create a hardware environment for the D'Claw turn task.
# `device_path` refers to the device port of the Dynamixel USB device.
# e.g. '/dev/ttyUSB0' for Linux, '/dev/tty.usbserial-*' for Mac OS.
env = gym.make('DClawTurnFixed-v0', device_path='/dev/ttyUSB0')
# Reset the environent and perform a random action.
env.reset()
env.step(env.action_space.sample())Not specifying the device_path i.e. env = gym.make('DClawTurnFixed-v0')
creates the simulated equivalent of the above hardware environment. The
simulated and hardware environments have the same interface.
To interactively render a simulation environment, run:
python -m robel.scripts.rollout -e DClawTurnFixed-v0 --render
# Also try this with other tasks such as DKittyWalkFixed-v0| Task | Description |
|---|---|
| Pose | Match a set of joint positions. |
| Turn | Turn an object to a specified angle. |
| Screw | Continuously rotate an object. |
| Task | Description |
|---|---|
| Stand | Stand upright. |
| Orient | Align heading with a target. |
| Walk | Walk to a target location. |
We designed ROBEL to be an easily extensible platform for new robots, tasks, and
benchmarks. See CONTRIBUTING.md for a guide
on how to contribute.
@misc{ahn2019robel,
title={ROBEL: Robotics Benchmarks for Learning with Low-Cost Robots},
author={Michael Ahn and Henry Zhu and Kristian Hartikainen and Hugo Ponte and Abhishek Gupta and Sergey Levine and Vikash Kumar},
year={2019},
eprint={1909.11639},
archivePrefix={arXiv},
primaryClass={cs.RO}
}
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