GAP: Generalized Adaptation Prior for Dexterous Piano Playing
Overview
This research introduces GAP, a sample-efficient adaptation framework designed to bridge the simulation-to-real gap in dexterous manipulation tasks. By training a gradient guidance network during simulation pretraining to learn how to adjust actions rather than just how to act, the method transfers an 'adaptation prior' to physical hardware. Evaluated on a dexterous hand performing piano playing, GAP achieves approximately 2.4 times the F1 score improvement of existing fast-adaptation baselines in simulation and improves F1 scores by 3.6%–23% across three musical pieces of varying difficulty on hardware after roughly 7 minutes of adaptation per piece.
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Reported quantity · F1 scores improvement in simulation: 2.4 other · Basis not reported
“ts. In simulation, GAP yields approximately 2.4 times the F1 scores improvement of the existing fast-adaptation baseline for dexterous piano playing under the same interaction budget. The learned prior also generalizes to musical pieces held out from pretraining. On a physical dexterous hand, the same simulation-pretrained prior improves F1 scores by 3.6%-23% across three pieces of varying difficulty after approximat”
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Reported quantity · adaptation time per execution: 7 other · Basis not reported
“ely 7 min of adaptation each. These results suggest a practical route to reduce the real-world interaction needed to deploy dexterous hands.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalGAP: Generalized Adaptation Prior for Dexterous Piano Playing
This research introduces GAP, a sample-efficient adaptation framework designed to bridge the simulation-to-real gap in dexterous manipulation tasks. By training a gradient guidance network during simulation pretraining to learn how to adjust actions rather than just how to act, the method transfers an 'adaptation prior' to physical hardware. Evaluated on a dexterous hand performing piano playing, GAP achieves approximately 2.4 times the F1 score improvement of existing fast-adaptation baselines in simulation and improves F1 scores by 3.6%–23% across three musical pieces of varying difficulty on hardware after roughly 7 minutes of adaptation per piece.
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