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Reinforcement learning is used to speed the production of behavior for the Atlas humanoid robot. At the heart of the learning process is a physics-based simulator that generates training data for various maneuvers. The control policy tracks and controls retargeted human motion data. Each maneuver is created with data from about 150 million simulator runs and transferred zero-shot to the hardware. This work is part of a collaboration between the RAI Institute and Boston Dynamics. Video Credit: RA
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Scientists have designed a reinforcement learning-based framework that enables multiple robot arms to perform up to 40 tasks simultaneously without colliding in a crowded workspace. Learn more in #ScienceRobotics: https://scim.ag/4mI8Y31 | Science
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