# Dexterous Robotic Piano Playing at Scale

arXiv: https://arxiv.org/abs/2511.02504 · HTML: https://mlsanity.com/paper/2511.02504/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2025.

## Built on by (most cited first, 1 of 1)

- [HandelBot: Real-World Piano Playing via Fast Adaptation of Dexterous Robot Policies](https://mlsanity.com/paper/2603.12243/) (2026)

## Abstract (arXiv)

Endowing robot hands with human-level dexterity has been a long-standing goal in robotics. Bimanual robotic piano playing represents a particularly challenging task: it is high-dimensional, contact-rich, and requires fast, precise control. We present OmniPianist, the first agent capable of performing nearly one thousand music pieces via scalable, human-demonstration-free learning. Our approach is built on three core components. First, we introduce an automatic fingering strategy based on Optimal Transport (OT), allowing the agent to autonomously discover efficient piano-playing strategies from scratch without demonstrations. Second, we conduct large-scale Reinforcement Learning (RL) by training more than 2,000 agents, each specialized in distinct music pieces, and aggregate their experience into a dataset named RP1M++, consisting of over one million trajectories for robotic piano playing. Finally, we employ a Flow Matching Transformer to leverage RP1M++ through large-scale imitation learning, resulting in the OmniPianist agent capable of performing a wide range of musical pieces. Extensive experiments and ablation studies highlight the effectiveness and scalability of our approach, advancing dexterous robotic piano playing at scale.