mojtabak

• Seyed Mojtaba Karbasi, Alexander Refsum Jensenius, Rolf Inge Godøy & Jim Tørresen (2024). Embodied intelligence for drumming; a reinforcement learning approach to drumming robots. Frontiers in Robotics and AI. 11. doi: 10.3389/frobt.2024.1450097. Fulltekst i vitenarkiv Vis sammendrag

  This paper investigates the potential of the intrinsically motivated reinforcement learning (IMRL) approach for robotic drumming. For this purpose, we implemented an IMRL-based algorithm for a drumming robot called ZRob, an underactuated two-DoF robotic arm with flexible grippers. Two ZRob robots were instructed to play rhythmic patterns derived from MIDI files. The RL algorithm is based on the deep deterministic policy gradient (DDPG) method, but instead of relying solely on extrinsic rewards, the robots are trained using a combination of both extrinsic and intrinsic reward signals. The results of the training experiments show that the utilization of intrinsic reward can lead to meaningful novel rhythmic patterns, while using only extrinsic reward would lead to predictable patterns identical to the MIDI inputs. Additionally, the observed drumming patterns are influenced not only by the learning algorithm but also by the robots’ physical dynamics and the drum’s constraints. This work suggests new insights into the potential of embodied intelligence for musical

• Seyed Mojtaba Karbasi, Alexander Refsum Jensenius, Rolf Inge Godøy & Jim Tørresen (2023). Exploring Emerging Drumming Patterns in a Chaotic Dynamical System using ZRob. I Miguel Ortiz & Adnan Marquez-Borbon (Red.), Proceedings of the International Conference on New Interfaces for Musical Expression. Universidad Autónoma Metropolitana. Fulltekst i vitenarkiv Vis sammendrag

  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.

• Seyed Mojtaba Karbasi, Alexander Refsum Jensenius, Rolf Inge Godøy & Jim Tørresen (2022). A Robotic Drummer with a Flexible Joint: the Effect of Passive Impedance on Drumming. I Romain Michon, Laurent Pottier & Yann Orlarey (Red.), Proceedings of the 19th Sound and Music Computing Conference. SMC Network. ISSN 9782958412609. s. 232–237. doi: 10.5281/zenodo.6797833. Fulltekst i vitenarkiv Vis sammendrag

  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.

• Seyed Mohammad Reza Sajadi, Seyed Mojtaba Karbasi, Henrik Brun, Jim Tørresen, Ole Jakob Elle & Kim Mathiassen (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. 9. doi: 10.3389/frobt.2022.896267. Fulltekst i vitenarkiv Vis sammendrag

  This paper presents the design, control, and experimental evaluation of a novel fully automated robotic-assisted system for the positioning and insertion of a commercial full core biopsy instrument under guidance by ultrasound imaging. The robotic system consisted of a novel 4° of freedom (DOF) add-on robot for the positioning and insertion of the biopsy instrument that is attached to a UR5-based teleoperation system with 6 DOF. The robotic system incorporates the advantages of both freehand and probe-guided biopsy techniques. The proposed robotic system can be used as a slave robot in a teleoperation configuration or as an autonomous or semi-autonomous robot in the future. While the UR5 manipulator was controlled using a teleoperation scheme with force controller, a reinforcement learning based controller using the Deep Deterministic Policy Gradient (DDPG) algorithm was developed for the add-on robotic system. The dexterous workspace analysis of the add-on robotic system demonstrated that the system has a suitable workspace within the US image. Two sets of comprehensive experiments including four experiments were performed to evaluate the robotic system’s performance in terms of the biopsy instrument positioning, and the insertion of the needle inside the ultrasound plane. The experimental results showed the ability of the robotic system for in-plane needle insertion. The overall mean error of all four experiments in the tracking of the needle angle was 0.446°, and the resolution of the needle insertion was 0.002 mm.

• Markus Toverud Ruud, Tale Hisdal Sandberg, Ulrik Johan Vedde Tranvaag, Benedikte Wallace, Seyed Mojtaba Karbasi & Jim Tørresen (2022). Reinforcement Learning Based Dance Movement Generation. I Kristin Carlson (Red.), MOCO '22: Proceedings of the 8th International Conference on Movement and Computing. Association for Computing Machinery (ACM). ISSN 9781450387163. doi: 10.1145/3537972.3538007. Fulltekst i vitenarkiv Vis sammendrag

  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.

• Seyed Mojtaba Karbasi, Rolf Inge Godøy, Alexander Refsum Jensenius & Jim Tørresen (2021). A Learning Method for Stiffness Control of a Drum Robot for Rebounding Double Strokes. I Dan Zhang (Red.), 2021 7th International Conference on Mechatronics and Robotics Engineering (ICMRE). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9780738132051. s. 54–58. doi: 10.1109/ICMRE51691.2021.9384843. Fulltekst i vitenarkiv Vis sammendrag

  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.

• Seyed Mojtaba Karbasi, Halvor Sogn Haug, Mia-Katrin Kvalsund, Michael Joseph Krzyzaniak & Jim Tørresen (2021). A Generative Model for Creating Musical Rhythms with Deep Reinforcement Learning. I Artemi-Maria Gioti (Red.), The Proceedings of 2nd Conference on AI Music Creativity. AI Music Creativity (AIMC). ISSN 9783200082724. doi: 10.5281/zenodo.5137900. Fulltekst i vitenarkiv Vis sammendrag

  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.

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• Seyed Mojtaba Karbasi, Silje Pileberg & Alexander Refsum Jensenius (2024). A newly developed robot can play the drums, listen, and learn. [Internet]. Science Norway. Fulltekst i vitenarkiv
• Seyed Mojtaba Karbasi, Alexander Refsum Jensenius, Rolf Inge Godøy & Jim Tørresen (2023). Exploring Emerging Drumming Patterns in a Chaotic Dynamical System using ZRob. Fulltekst i vitenarkiv Vis sammendrag

  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.

• Seyed Mojtaba Karbasi (2023). Reinforcement Learning for Curious Systems. Fulltekst i vitenarkiv
• Seyed Mojtaba Karbasi, Alexander Refsum Jensenius, Rolf Inge Godøy & Jim Tørresen (2022). A Robotic Drummer with a Flexible Joint: the Effect of Passive Impedance on Drumming. Fulltekst i vitenarkiv Vis sammendrag

  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.

• Markus Toverud Ruud, Tale Hisdal Sandberg, Ulrik Johan Vedde Tranvaag, Benedikte Wallace, Seyed Mojtaba Karbasi & Jim Tørresen (2022). Reinforcement Learning Based Dance Movement Generation. Fulltekst i vitenarkiv Vis sammendrag

  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.

• Seyed Mojtaba Karbasi (2021). Creativity, Fun, and Intrinsic Motivation. Fulltekst i vitenarkiv
• Seyed Mojtaba Karbasi, Halvor Sogn Haug, Mia-Katrin Kvalsund, Michael Joseph Krzyzaniak & Jim Tørresen (2021). A Generative Model for Creating Musical Rhythms with Deep Reinforcement Learning. Fulltekst i vitenarkiv
• Seyed Mojtaba Karbasi, Rolf Inge Godøy, Alexander Refsum Jensenius & Jim Tørresen (2021). A Learning Method for Stiffness Control of a Drum Robot for Rebounding Double Strokes. Fulltekst i vitenarkiv Vis sammendrag

  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.

Se alle arbeider i NVA