Summary of Pianomime: Learning a Generalist, Dexterous Piano Player From Internet Demonstrations, by Cheng Qian et al.
PianoMime: Learning a Generalist, Dexterous Piano Player from Internet Demonstrations
by Cheng Qian, Julen Urain, Kevin Zakka, Jan Peters
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The PianoMime framework trains piano-playing agents using internet demonstrations, leveraging YouTube videos of professional pianists to learn a generalist agent capable of playing any song. The framework consists of three phases: data preparation, policy learning from demonstrations, and policy distillation into a single agent. Various policy designs are explored, and the impact of training data on the agent’s generalization capabilities is evaluated. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PianoMime is a new way to teach robots how to play the piano by using YouTube videos as lessons. The system has three steps: first, it prepares the data from the YouTube videos, then trains expert policies for each song, and finally distills those policies into a single generalist agent that can play any song. The team tested different approaches and found that they could train an agent to play new songs with up to 56% accuracy. |
Keywords
» Artificial intelligence » Distillation » Generalization
