Package: robel
ROBEL:RoboticsBenchmarks forLearningROBEL 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 toROBEL's webpagefor
full details.Robot PlatformsROBEL 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'Clawis a nine degree of freedom manipulation platform capable of
performing dexterous manipulation.D'Kittyis a twelve degree of freedom quadruped capable of agile
locomotion.FeaturesGym Compliant-- ROBEL environments are fullyGym-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 byMuJoCo.Hardware interface-- ROBEL is built using Dynamixel motors and
communicates with the hardware device through theDynamixelSDK.External tracking support-- For D'Kitty environments, external tracking
is supported throughOpenVRtracking.Open-source design-- The hardware design and build instructions are
fully open-sourced and are available for anyone to build their own robots.Getting started1. Hardware assemblyPlease refer to theHardware Guidefor getting
started with the ROBEL hardware platforms.2. Software installation2.1 MuJoCoDownload MuJoCo Pro 2.00 from theMuJoCo 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 formujoco_pyto install properly:exportLD_LIBRARY_PATH=$LD_LIBRARY_PATH:$HOME/.mujoco/mujoco200/binRunsource ~/.bashrcafterwards.2.2 ROBELROBEL requires Python 3.5 or higher. You can install ROBEL by running:pipinstallrobelWe recommend doing this in avirtualenvor a Conda environment to avoid
interfering with system dependencies or existing packages.Alternatively, you can install directly from the repository by running:gitclone--recurse-submoduleshttps://github.com/google-research/robel.git
pipinstall-erobel/To run on hardware, additionally install theDynamixelSDKPython API:pipinstallgit+https://github.com/ROBOTIS-GIT/DynamixelSDK.git#subdirectory=python2.3 Example Usageimportrobelimportgym# 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 thedevice_pathi.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-mrobel.scripts.rollout-eDClawTurnFixed-v0--render# Also try this with other tasks such as DKittyWalkFixed-v0Benchmark TasksD'ClawTaskDescriptionPoseMatch a set of joint positions.TurnTurn an object to a specified angle.ScrewContinuously rotate an object.D'KittyTaskDescriptionStandStand upright.OrientAlign heading with a target.WalkWalk to a target location.ContributingWe designed ROBEL to be an easily extensible platform for new robots, tasks, and
benchmarks. SeeCONTRIBUTING.mdfor a guide
on how to contribute.Citation@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}
}DisclaimerThis is not an official Google product.