Seyed Mojtaba Karbasi

Student - Faculty of Mathematics and Natural Sciences

Norwegian version of this page

Email mojtabak@ifi.uio.no

Mobile phone +47 452 32 483

Username

Visiting address Sem Sælands vei 24 Fysikkbygningen 0371 Oslo

Postal address Postboks 1032 Blindern 0315 Oslo

Other affiliations RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion (IFI)

Press photo Download business card

Bio

Mojtaba Karbasi is a PhD fellow at RITMO Centre at the University of Oslo. His research is focused on robot control and learning, reinforcement learning, cognitive robotics, and motor control theories. He is currently working on drum robots which are designed to learn drumming tasks through interaction with the environment. In this work, he is exploring reinforcement learning methods and motor control models to develop an interactive robotic system for playing the drum. Learn more about his research on the drum robot "ZRob" here.

Background

M.Sc. in Control Systems Engineering, School of Electrical and Computer Engineering, College of Engineering, University of Tehran
B.Sc in Electrical Engineering, Department of Electrical Engineering, Amirkabir University of Technology (Tehran Polytechnic)

Teaching

IN5490/IN9490 - Advanced Topics in Artificial Intelligence for Intelligent Systems
IN3160/IN4160 - Digital system design
MUS4089 - Research Internship at the Department of Musicology

Academic interests

Robotics
Rhythmic Movements
Control Systems
Motor Control
Machine Learning
Artificial Intelligence

Tags: Robotics, Rhythmic Movements, Artificial Intelligence, Machine Learning, Motor Control

Karbasi, Seyed Mojtaba; Jensenius, Alexander Refsum; Godøy, Rolf Inge & Tørresen, Jim (2023). Exploring Emerging Drumming Patterns in a Chaotic Dynamical System using ZRob. In Ortiz, Miguel & Marquez-Borbon, Adnan (Ed.), Proceedings of the International Conference on New Interfaces for Musical Expression. Universidad Autónoma Metropolitana. ISSN 2220-4792. Full text in Research Archive Show summary
ZRob is a robotic system designed for playing a snare drum. The robot is constructed with a passive flexible spring-based joint inspired by the human hand. This paper describes a study exploring rhythmic patterns by exploiting the chaotic dynamics of two ZRobs. In the experiment, we explored the control configurations of each arm by trying to create un- predictable patterns. Over 200 samples have been recorded and analyzed. We show how the chaotic dynamics of ZRob can be used for creating new drumming patterns.
Ruud, Markus Toverud; Sandberg, Tale Hisdal; Tranvaag, Ulrik Johan Vedde; Wallace, Benedikte; Karbasi, Seyed Mojtaba & Tørresen, Jim (2022). Reinforcement Learning Based Dance Movement Generation. In Carlson, Kristin (Eds.), MOCO '22: Proceedings of the 8th International Conference on Movement and Computing. Association for Computing Machinery (ACM). ISSN 978-1-4503-8716-3. doi: 10.1145/3537972.3538007. Full text in Research Archive Show summary
Generating genuinely creative and novel artifacts with machine learning is still a challenge in the world of computational science. A creative machine learning agent can be beneficial for applications where novel solutions are desired and may also optimize search. Reinforcement Learnings’ (RL) interactive properties can make it an effective tool to investigate these possibilities in creative contexts. This paper shows how a Reinforcement learning-based technique, in combination with Principal Component Analysis (PCA), can be utilized for generating varying movements based on a goal picking policy. The proposed model is trained on a data set of motion capture recordings of dance improvisation. Our study shows that the trained RL agent can learn to pick sequences of dance poses that are coherent, have compound movement, and can resemble dance.
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 Show summary
Intelligent robots aimed for performing music and playing musical instruments have been developed in recent years. With the advancements in artificial intelligence and robotic systems, new capabilities have been explored in this field. One major aspect of musical robots that can lead to the emergence of creative results is the ability to learn skills autonomously. To make it feasible, it is important to make the robot utilize its maximum potential and mechanical capabilities to play a musical instrument. Furthermore, the robot needs to find the musical possibilities based on the physical properties of the instrument to provide satisfying results. In this work, we introduce a drum robot with certain mechanical specifications and analyze the capabilities of the robot according to the drumming sound results of the robot. The robot has two degrees of freedom, actuated by one quasi direct-drive servo motor. The gripper of the robot features a flexible joint with passive springs which adds complexity to the movements of the drumstick. In a basic experiment, we have looked at the drum roll performance by the robot while changing a few control variables such as frequency and amplitude of the motion. Both single-stroke and double-stroke drum rolls can be performed by the robot by changing the control variables. The effect of the flexible gripper on the drumming results of the robot is the main focus of this study. Additionally, we have divided the control space according to the type of drum rolls. The results of this experiment lay the groundwork for developing an intelligent algorithm for the robot to learn musical patterns by interacting with the drum.
Sajadi, Seyed Mohammad Reza; Karbasi, Seyed Mojtaba; Brun, Henrik; Tørresen, Jim; Elle, Ole Jakob & Mathiassen, Kim (2022). Towards Autonomous Robotic Biopsy—Design, Modeling and Control of a Robot for Needle Insertion of a Commercial Full Core Biopsy Instrument. Frontiers in Robotics and AI. ISSN 2296-9144. 9. doi: 10.3389/frobt.2022.896267. 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 International Conference on AI and Musical Creativity. ISSN 978-3-200-08272-4. doi: 10.5281/zenodo.5137900. Full text in Research Archive Show summary
Musical Rhythms can be modeled in different ways. Usually the models rely on certain temporal divisions and time discretization. We have proposed a generative model based on Deep Reinforcement Learning (Deep RL) that can learn musical rhythmic patterns without defining temporal structures in advance. In this work we have used the Dr. Squiggles platform, which is an interactive robotic system that generates musical rhythms via interaction, to train a Deep RL agent. The goal of the agent is to learn the rhythmic behavior from an environment with high temporal resolution, and without defining any basic rhythmic pattern for the agent. This means that the agent is supposed to learn rhythmic behavior in an approximated continuous space just via interaction with other rhythmic agents. The results show significant adaptability from the agent and great potential for RL-based models to be used as creative algorithms in musical and creativity applications.
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 (Institute of Electrical and Electronics Engineers). ISSN 978-0-7381-3205-1. p. 54–58. doi: 10.1109/ICMRE51691.2021.9384843. Full text in Research Archive Show summary
In robot drumming, performing double stroke rolls is a key ability. Human drummers learn to play double strokes by just trying it several times. For performing it, a model needs to be learned to provide anticipatory commands during drumming. Joint stiffness plays a key role in rebounding double stroke task and should be considered in the model. We have introduced an interactive learning method for a drum robot to learn joint stiffness for rebounding double stroke task. The model is simulated for a 2-DoF robotic arm. The algorithm is simulated with 3 different drum kits to show the robustness of the learning approach. The simulation results also show significant compatibility with human performance results. In addition, the refined learning algorithm adjusts the stroke timing which is important for producing proper rhythms.

View all works in Cristin

Karbasi, Seyed Mojtaba (2023). Reinforcement Learning for Curious Systems.
Karbasi, Seyed Mojtaba; Jensenius, Alexander Refsum; Godøy, Rolf Inge & Tørresen, Jim (2023). Exploring Emerging Drumming Patterns in a Chaotic Dynamical System using ZRob. Show summary
ZRob is a robotic system designed for playing a snare drum. The robot is constructed with a passive flexible spring-based joint inspired by the human hand. This paper describes a study exploring rhythmic patterns by exploiting the chaotic dynamics of two ZRobs. In the experiment, we explored the control configurations of each arm by trying to create un- predictable patterns. Over 200 samples have been recorded and analyzed. We show how the chaotic dynamics of ZRob can be used for creating new drumming patterns.
Ruud, Markus Toverud; Sandberg, Tale Hisdal; Tranvaag, Ulrik Johan Vedde; Wallace, Benedikte; Karbasi, Seyed Mojtaba & Tørresen, Jim (2022). Reinforcement Learning Based Dance Movement Generation. Show summary
Generating genuinely creative and novel artifacts with machine learning is still a challenge in the world of computational science. A creative machine learning agent can be beneficial for applications where novel solutions are desired and may also optimize search. Reinforcement Learnings’ (RL) interactive properties can make it an effective tool to investigate these possibilities in creative contexts. This paper shows how a Reinforcement learning-based technique, in combination with Principal Component Analysis (PCA), can be utilized for generating varying movements based on a goal picking policy. The proposed model is trained on a data set of motion capture recordings of dance improvisation. Our study shows that the trained RL agent can learn to pick sequences of dance poses that are coherent, have compound movement, and can resemble dance.
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. Show summary
Intelligent robots aimed for performing music and playing musical instruments have been developed in recent years. With the advancements in artificial intelligence and robotic systems, new capabilities have been explored in this field. One major aspect of musical robots that can lead to the emergence of creative results is the ability to learn skills autonomously. To make it feasible, it is important to make the robot utilize its maximum potential and mechanical capabilities to play a musical instrument. Furthermore, the robot needs to find the musical possibilities based on the physical properties of the instrument to provide satisfying results. In this work, we introduce a drum robot with certain mechanical specifications and analyze the capabilities of the robot according to the drumming sound results of the robot. The robot has two degrees of freedom, actuated by one quasi direct-drive servo motor. The gripper of the robot features a flexible joint with passive springs which adds complexity to the movements of the drumstick. In a basic experiment, we have looked at the drum roll performance by the robot while changing a few control variables such as frequency and amplitude of the motion. Both single-stroke and double-stroke drum rolls can be performed by the robot by changing the control variables. The effect of the flexible gripper on the drumming results of the robot is the main focus of this study. Additionally, we have divided the control space according to the type of drum rolls. The results of this experiment lay the groundwork for developing an intelligent algorithm for the robot to learn musical patterns by interacting with the drum.
Karbasi, Seyed Mojtaba (2021). Creativity, Fun, and Intrinsic Motivation.
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.
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. Show summary
In robot drumming, performing double stroke rolls is a key ability. Human drummers learn to play double strokes by just trying it several times. For performing it, a model needs to be learned to provide anticipatory commands during drumming. Joint stiffness plays a key role in rebounding double stroke task and should be considered in the model. We have introduced an interactive learning method for a drum robot to learn joint stiffness for rebounding double stroke task. The model is simulated for a 2-DoF robotic arm. The algorithm is simulated with 3 different drum kits to show the robustness of the learning approach. The simulation results also show significant compatibility with human performance results. In addition, the refined learning algorithm adjusts the stroke timing which is important for producing proper rhythms.