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Robots can now learn complex dexterous tasks at human-level speed without repetitive skill training, thanks to a novel pre-training and post-training reinforcement learning approach.
Turns out, the best memory design for robotic manipulation depends heavily on the task, with no single architecture dominating across the board.
Achieve 93% success in zero-shot sim-to-real transfer for contact-rich tasks by realistically simulating tactile shear, blowing away prior art.
Imagine giving robots a sense of touch as sensitive as a spiderweb, using nothing more than vibrating strings and microphones.
