Meet RoboPianist: A New Benchmarking Suite for High-Dimensional Control in Piano Mastery with Simulated Robotic Hands

The measurement process in the control and reinforcement domains of learning progress is quite challenging. One particularly neglected area has been robust benchmarks that focus on high-dimensional control, including, in particular, the perhaps ultimate “challenge problem” of high-dimensional robotics: mastering bimanual (two-handed) control. with several fingers. At the same time, some benchmarking efforts in control and reinforcement learning have started to add up and explore different aspects of depth. Despite decades of research to mimic the dexterity of the human hand, controlling large dimensions in robots remains a major challenge.

A group of researchers from UC Berkeley, Google, DeepMind, Stanford University, and Simon Fraser University present a new reference suite for high-dimensional control called ROBOPIANIST. In his work, simulated bimanual anthropomorphic robotic hands are tasked with playing various songs conditioned on sheet music on a musical instrument digital interface (MIDI) transcription. The robot hands have 44 total actuators and 22 actuators per hand, similar to how human hands are slightly underactivated.

Playing a song well requires being able to sequence actions in ways that exhibit many of the qualities of high-dimensional control politics. This includes:

JOIN the fastest ML subreddit community

1. Spatial and temporal precision.
2. Coordination of 2 hands and ten fingers.
3. Strategic keystroke planning to facilitate other keystrokes

150 songs comprise the original ROBOPIANIST-repertoire-150 benchmark, each serving as a standalone virtual work. Researchers study the performance of model-based and model-free methods through comprehensive experiments such as model-free (RL) and model-based (MPC) methods. The results suggest that, despite having a lot of room for improvement, the proposed policies can produce strong performances.

A policy’s ability to learn a song can be used to rank songs (ie tasks) by difficulty. The researchers believe that the ability to group tasks according to such criteria may encourage further study in a variety of areas related to robot learning, such as curriculum and transfer learning. RoboPianist offers exciting opportunities for various study approaches, such as learning by imitation, multi-task learning, zero-throw generalization, and multimodal (sound, sight, and touch) learning. Overall, ROBOPIANIST shows a simple goal, an environment that is easy to replicate, clear evaluation criteria, and is open to various extension potentials in the future.

review the Paper, Project and Github. All credit for this research goes to the researchers of this project. Also, don’t forget to join our 18k+ ML SubReddit, discord channeland electronic newsletterwhere we share the latest AI research news, exciting AI projects, and more.