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Embodied intelligence for drumming; a reinforcement learning approach to drumming robots.

Seyed Mojtaba Karbasi^1,2, Alexander Refsum Jensenius^1,3, Rolf Inge Godøy^1,3

¹RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Oslo, Norway.

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Summary

Intrinsically motivated reinforcement learning (IMRL) enables robots to create novel rhythmic drumming patterns. This approach, unlike purely extrinsic rewards, allows for more creative and less predictable musical outputs by robots.

Keywords:

drumming embodied intelligence intrinsic motivation musical robots reinforcement learning

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Area of Science:

Robotics
Artificial Intelligence
Music Technology

Background:

Robotic musical performance is an emerging field.
Reinforcement learning (RL) has been applied to control robotic systems.
Intrinsically motivated reinforcement learning (IMRL) offers a framework for autonomous exploration and creativity.

Purpose of the Study:

To investigate the efficacy of IMRL for robotic drumming.
To explore the generation of novel rhythmic patterns by robots.
To understand the influence of embodied intelligence on musical expression.

Main Methods:

Implementation of an IMRL algorithm for the ZRob robotic arm.
Utilizing the deep deterministic policy gradient (DDPG) method with combined extrinsic and intrinsic rewards.
Training two ZRob robots to play rhythmic patterns derived from MIDI files.

Main Results:

IMRL generated meaningful novel rhythmic patterns.
Exclusive use of extrinsic rewards resulted in predictable, MIDI-identical patterns.
Robotic physical dynamics and drum constraints influenced drumming patterns.

Conclusions:

IMRL is a promising approach for creative robotic musical performance.
Embodied intelligence plays a significant role in robotic musical expression.
This research opens new avenues for human-robot musical collaboration.