https://arxiv.org/abs/2106.02040

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Computer Science > Robotics

arXiv:2106.02040 (cs)
[Submitted on 3 Jun 2021 (v1), last revised 8 Jun 2021 (this version,
v2)]

Title:Towards Learning to Play Piano with Dexterous Hands and Touch

Authors:Huazhe Xu, Yuping Luo, Shaoxiong Wang, Trevor Darrell,
Roberto Calandra
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    Abstract: The virtuoso plays the piano with passion, poetry and
    extraordinary technical ability. As Liszt said (a virtuoso)must
    call up scent and blossom, and breathe the breath of life. The
    strongest robots that can play a piano are based on a combination
    of specialized robot hands/piano and hardcoded planning
    algorithms. In contrast to that, in this paper, we demonstrate
    how an agent can learn directly from machine-readable music score
    to play the piano with dexterous hands on a simulated piano using
    reinforcement learning (RL) from scratch. We demonstrate the RL
    agents can not only find the correct key position but also deal
    with various rhythmic, volume and fingering, requirements. We
    achieve this by using a touch-augmented reward and a novel
    curriculum of tasks. We conclude by carefully studying the
    important aspects to enable such learning algorithms and that can
    potentially shed light on future research in this direction.

Subjects: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine
          Learning (stat.ML)
Cite as:  arXiv:2106.02040 [cs.RO]
          (or arXiv:2106.02040v2 [cs.RO] for this version)

Submission history

From: Huazhe Xu [view email]
[v1] Thu, 3 Jun 2021 17:59:31 UTC (3,363 KB)
[v2] Tue, 8 Jun 2021 23:09:52 UTC (3,295 KB)
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Huazhe Xu
Yuping Luo
Shaoxiong Wang
Trevor Darrell
Roberto Calandra
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