Google AI Blog: ROBEL: Robotics Benchmarks for Learning with Low-Cost Robots

ROBEL: Robotics Benchmarks for Learning with Low-Cost RobotsWednesday, October 9, 2019Posted by Michael Ahn, Software Engineer and Vikash Kumar, Research Scientist, Robotics at GoogleLearning-based methods for solving robotic control problems have recently seen significant momentum, driven by the widening availability of simulated benchmarks (like dm_control or OpenAI-Gym) and advancements in flexible and scalable reinforcement learning techniques (DDPG, QT-Opt, or Soft Actor-Critic). While learning through simulation is effective, these simulated environments often encounter difficulty in deploying to real-world robots due to factors such as inaccurate modeling of physical phenomena and system delays. This motivates the need to develop robotic control solutions directly in the real world, on real physical hardware.

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