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ROBEL: Robotics Benchmarks for Learning with Low-Cost Robots
ROBEL is an open-source platform of cost-effective robots designed for r...
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Learning to Drive Small Scale Cars from Scratch
We consider the problem of learning to drive low-cost small scale cars u...
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TriFinger: An Open-Source Robot for Learning Dexterity
Dexterous object manipulation remains an open problem in robotics, despi...
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KRATOS: An Open Source Hardware-Software Platform for Rapid Research in LPWANs
Long-range (LoRa) radio technologies have recently gained momentum in th...
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NimbRo-OP2X: Adult-sized Open-source 3D Printed Humanoid Robot
Humanoid robotics research depends on capable robot platforms, but recen...
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LICSTER – A Low-cost ICS Security Testbed for Education and Research
Unnoticed by most people, Industrial Control Systems (ICSs) control enti...
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Horizon: Facebook's Open Source Applied Reinforcement Learning Platform
In this paper we present Horizon, Facebook's open source applied reinfor...
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RealAnt: An Open-Source Low-Cost Quadruped for Research in Real-World Reinforcement Learning
Current robot platforms available for research are either very expensive or unable to handle the abuse of exploratory controls in reinforcement learning. We develop RealAnt, a minimal low-cost physical version of the popular 'Ant' benchmark used in reinforcement learning. RealAnt costs only 410 in materials and can be assembled in less than an hour. We validate the platform with reinforcement learning experiments and provide baseline results on a set of benchmark tasks. We demonstrate that the TD3 algorithm can learn to walk the RealAnt from less than 45 minutes of experience. We also provide simulator versions of the robot (with the same dimensions, state-action spaces, and delayed noisy observations) in the MuJoCo and PyBullet simulators. We open-source hardware designs, supporting software, and baseline results for ease of reproducibility.
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