RP1M: A Large-Scale Motion Dataset for Piano Playing with Bimanual Dexterous Robot Hands

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A4 Artikkeli konferenssijulkaisussa
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Date

2025

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Department of Electrical Engineering and Automation
Department of Computer Science

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Language

en

Pages

20

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Proceedings of Machine Learning Research, Volume 270, pp. 5184-5203

Abstract

It has been a long-standing research goal to endow robot hands with human-level dexterity. Bimanual robot piano playing constitutes a task that combines challenges from dynamic tasks, such as generating fast while precise motions, with slower but contact-rich manipulation problems. Although reinforcement learning-based approaches have shown promising results in single-task performance, these methods struggle in a multi-song setting. Our work aims to close this gap and, thereby, enable imitation learning approaches for robot piano playing at scale. To this end, we introduce the Robot Piano 1 Million (RP1M) dataset, containing bimanual robot piano playing motion data of more than one million trajectories. We formulate finger placements as an optimal transport problem, thus, enabling automatic annotation of vast amounts of unlabeled songs.

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Publisher Copyright: © 2024 Proceedings of Machine Learning Research.

Keywords

Bimanual dexterous robot hands, dataset for robot piano playing, imitation learning, robot learning at scale

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Citation

Zhao, Y, Chen, L, Schneider, J, Gao, Q, Kannala, J, Schölkopf, B, Pajarinen, J & Büchler, D 2025, ' RP1M: A Large-Scale Motion Dataset for Piano Playing with Bimanual Dexterous Robot Hands ', Proceedings of Machine Learning Research, vol. 270, pp. 5184-5203 . < https://proceedings.mlr.press/v270/zhao25d.html >

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https://urn.fi/URN:NBN:fi:aalto-202503263052

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[article-cris] Perustieteiden korkeakoulu / SCI