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Expressive Robotic Pianist: Mastering Complex Piano Repertoire with Graph-Mimic and Musical Dynamics — arXiv:2609.10844

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.10844
License (as archived): CC0-1.0
Archived: 2026-10-16T06:35:54.460Z
SHA-256 of the archived text: 9aeccb970328acb749682e546b6758eaffd6ecec20f986c00b1ae59930697054
Stable URL of this page: https://metraipolis.com/documento/dc_6bcaf7269754

How to cite

Expressive Robotic Pianist: Mastering Complex Piano Repertoire with Graph-Mimic and Musical Dynamics — arXiv:2609.10844 — arXiv (en), https://arxiv.org/abs/2609.10844 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_6bcaf7269754 (Metraipolis archive dc_6bcaf7269754, sha256 9aeccb970328acb749682e546b6758eaffd6ecec20f986c00b1ae59930697054)

BibTeX

@misc{dc6bcaf7269754,
  title = {Expressive Robotic Pianist: Mastering Complex Piano Repertoire with Graph-Mimic and Musical Dynamics — arXiv:2609.10844},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.10844},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_6bcaf7269754; sha256 9aeccb970328acb749682e546b6758eaffd6ecec20f986c00b1ae59930697054},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.10844. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Enabling robots to perform musical instruments with human-level expressivity represents a frontier in bridging the gap between mechanical precision and artistic interpretation. Despite advances in robotic dexterity, replicating the fluid finger transitions and nuanced dynamic control characteristic of human pianists remains a significant challenge. Through a reinforcement learning-based control framework, we demonstrate that a dexterous robotic hand can achieve high-fidelity performance across a diverse piano repertoire. Central to our approach is a graph-based optimization strategy that guides the robot to generate natural pre-press and key-press fingering strategies that closely resemble human movement patterns. To achieve expressive sound production, the control system is coupled with a physics-inspired acoustic model that modulates keypress velocity to accurately reproduce the dynamic variations specified in musical scores. Quantitative evaluations demonstrate that our expressive control model significantly outperforms baseline methods in both finger morphology similarity and dynamic velocity accuracy. In a perceptual test involving participants from diverse listener groups, performances generated by our system are significantly preferred over baseline robotic performances and are indistinguishable from human performances for non-professional audiences. Furthermore, extensive experiments across multiple musical styles confirm that our method maintains high note-level accuracy while achieving expressive performance. Our approach provides a robust pathway for robotic systems to move beyond mere mechanical accuracy, elevating robotic musicianship to a level of expressive performance comparable to human pianists.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-11 in cs.RO).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2609.10844 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T06:10:18.202Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-09-11?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).