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Rheinland-Pfälzische Technische Universität (RPTU) Kaiserslautern, 67663, Kaiserslautern, Germany
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Fine-tuning policies transferred between different robots slashes training time for peg-in-hole tasks, outperforming both zero-shot transfer and training from scratch.
Robots can now learn to reliably assemble parts despite uncertainty, thanks to a reinforcement learning approach that breaks down the task into reusable skills.