The project develops an interactive robotic system for learning and playing the drum.
One robot prototype with a flexible gripper. This is the smaller design with low-stiffness springs used for low-speed drumming.
About the project
This is a robotic platform for developing an intelligent system for playing the drum. The project involves the mechanical design of the robotic arm, prototyping, control, and artificial intelligence used for learning drumming tasks.
All the body parts are designed and 3D-printed using state-of-the-art prototyping tools. The robot grippers contain passive springs for exploiting the natural dynamics of the drum membrane and the drum stick. One aim of the project is to explore different mechanical characteristics of the robot body such as stiffness of the gripper.
The robots use quasi-direct drive servo motors, capable of torque adjustment with internal position, velocity and torque control modes. The control problem of the robots involves trajectory planning, sound-motion mapping, stable frequency and amplitude range of motion, and frequency adaptation.
The primary purpose of the intelligent algorithm of the robotic system is to learn musical patterns and creative behaviour in drumming. The main approach for this purpose is Reinforcement Learning which makes the system capable of interacting with the environment.
The aim of the project is to develop a robotic system that can learn drumming tasks through interaction with the environment. The interaction is based on adaptation to different physical constraints in the robot body and the dynamical behaviour of the environment. Some practical challenges involve synchronisation, motor learning, sound-motion mapping, and real-time training.
- Analysis by Synthesis
- Reinforcement Learning
- Intermittent Control
- Curiosity-based Learning
Codes and design
Karbasi, Seyed Mojtaba; Jensenius, Alexander Refsum; Godøy, Rolf Inge & Tørresen, Jim (2022). A Robotic Drummer with a Flexible Joint: the Effect of Passive Impedance on Drumming. In Michon, Romain; Pottier, Laurent & Orlarey, Yann (Ed.), Proceedings of the 19th Sound and Music Computing Conference. SMC Network. ISSN 9782958412609. p. 232–237. doi: 10.5281/zenodo.6797833. Full text in Research Archive
Karbasi, Seyed Mojtaba; Godøy, Rolf Inge; Jensenius, Alexander Refsum & Tørresen, Jim (2021). A Learning Method for Stiffness Control of a Drum Robot for Rebounding Double Strokes. In Zhang, Dan (Eds.), 2021 7th International Conference on Mechatronics and Robotics Engineering (ICMRE). IEEE. ISSN 978-0-7381-3205-1. p. 54–58. doi: 10.1109/ICMRE51691.2021.9384843. Full text in Research Archive
Karbasi, Seyed Mojtaba; Haug, Halvor Sogn; Kvalsund, Mia-Katrin; Krzyzaniak, Michael Joseph & Tørresen, Jim (2021). A Generative Model for Creating Musical Rhythms with Deep Reinforcement Learning. In Gioti, Artemi-Maria (Eds.), The Proceedings of 2nd Conference on AI Music Creativity. Proceedings of Joint Conference on AI Music Creativity (CSMC + MuMe). ISSN 978-3-200-08272-4. doi: 10.5281/zenodo.5137900. Full text in Research Archive