Kyrre Glette

• Bruin, Ege de; Glette, Kyrre & Ellefsen, Kai Olav (2026). Lamarckian Inheritance in Dynamic Environments: How Key Variables Affect Evolutionary Dynamics. ALIFE : Proceedings of the Artificial Life Conference. isal2026. doi: https:/direct.mit.edu/isal/proceedings/isal2026/38/35/138242. Full text in Research Archive
• Bruin, Ege de; Glette, Kyrre; Ellefsen, Kai; Nadizar, Giorgia & Medvet, Eric (2026). Social Learning Strategies for Evolved Virtual Soft Robots. In Trujillo, Leonardo & Hu, Ting (Ed.), GECCO '26: Proceedings of the Genetic and Evolutionary Computation Conference. Association for Computing Machinery (ACM). ISSN 9798400724879. p. 96–105. doi: 10.1145/3795095.3805149. Full text in Research Archive
• Kvalsund, Mia-Katrin Ose; Ellefsen, Kai Olav; Glette, Kyrre; Pontes-Filho, Sidney & Lepperød, Mikkel Elle (2026). Sensor movement drives emergent attention and scalability in active neural cellular automata. Neural Networks. ISSN 0893-6080. 200. doi: 10.1016/j.neunet.2026.108798. Full text in Research Archive
• Bruin, Ege de; Glette, Kyrre & Ellefsen, Kai Olav (2025). Integrating Sample Inheritance into Bayesian Optimization for Evolutionary Robotics. In Witkowski, Olaf; Adams, Alyssa M.; Sinapayen, Lana; Baltieri, Manuel & Khosravy, Mahdi (Ed.), ALIFE 2025: Ciphers of Life: Proceedings of the Artificial Life Conference 2025. MIT Press. doi: 10.1162/ISAL.a.866. Full text in Research Archive Show summary

  In evolutionary robotics, robot morphologies are designed automatically using evolutionary algorithms. This creates a body-brain optimization problem, where both morphology and control must be optimized together. A common approach is to include controller optimization for each morphology, but starting from scratch for every new body may require a high controller learning budget. We address this by using Bayesian optimization for controller optimization, exploiting its sample efficiency and strong exploration capabilities, and using sample inheritance as a form of Lamarckian inheritance. Under a deliberately low controller learning budget for each morphology, we investigate two types of sample inheritance: (1) transferring all the parent’s samples to the offspring to be used as prior without evaluating them, and (2) reevaluating the parent’s best samples on the offspring. Both are compared to a baseline without inheritance. Our results show that reevaluation performs best, with prior-based inheritance also outperforming no inheritance. Analysis reveals that while the learning budget is too low for a single morphology, generational inheritance compensates for this by accumulating learned adaptations across generations. Furthermore, inheritance mainly benefits offspring morphologies that are similar to their parents. Finally, we demonstrate the critical role of the environment, with more challenging environments resulting in more stable walking gaits. Our findings highlight that inheritance mechanisms can boost performance in evolutionary robotics without needing large learning budgets, offering an efficient path toward more capable robot design.

• Bravo, Pedro Pablo Lucas; Szorkovszky, Alexander; Fasciani, Stefano & Glette, Kyrre (2025). HS-ims: A Platform for Human-Swarm Interactive Music Systems. In Tei, Kenji & Zambonelli, Franco (Ed.), 2025 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331502157. doi: 10.1109/ACSOS-C66519.2025.00067. Full text in Research Archive Show summary

  This paper introduces HS-ims, an integrated virtual environment designed for developing real-time musical multiagent systems. It addresses the need for technological platforms that emphasize sound applications, flexible prototyping, and comprehensive data collection from these multi-agent systems. HS-ims offers a 3D virtual environment with built-in sound synthesis via binaural audio, an Open Sound Control (OSC) API implementation that is language-agnostic, and a data collection module. The platform operates with an External Controller managing multi-agent logic. We present relevant performance metrics and provide examples highlighting how HS-ims has proven valuable in research and education. This encourages further exploration and establishes it as a useful tool for music performance.

• Jónsson, Björn Thór; Erdem, Cagri; Fasciani, Stefano & Glette, Kyrre (2025). Live Coding the Lineage. In Sedo, Anna Xambo & Buffa, Michel (Ed.), Proceedings of the International Web Audio Conference (WAC). IRCAM. doi: 10.5281/zenodo.17642435. Full text in Research Archive Show summary

  This paper presents a web-based system for exploring and performing with sounds discovered through Quality Diver- sity evolutionary algorithms. Our system renders interactive phylogenetic trees of evolutionary sound discovery processes, transforming genealogical relationships into a performable interface—the “Harpsitree”—using trajectory-based interaction and automatic code generation. The core concept we explore is “algorithmic sketching,” where trajectory-based discovery through evolutionary structures progressively formalises into executable code, bridging visual exploration and textual live coding paradigms. This work contributes a novel performance interface as part of a larger research project investigating evolutionary computation for sound discovery, demonstrating how phylogenetic structures can reveal com- positional affordances through appropriate interaction design.

• Bruin, Ege de; Glette, Kyrre & Ellefsen, Kai Olav (2025). Generational Replacement and Learning for High-Performing and Diverse Populations in Evolvable Robots. In Obafemi-Ajayi, Tayo (Eds.), 2025 IEEE Symposium on Computational Intelligence in Artificial Life and Cooperative Intelligent Systems. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331508388. doi: 10.1109/alife-cis64968.2025.10979828. Full text in Research Archive Show summary

  Evolutionary Robotics offers the possibility to design robots to solve a specific task automatically by optimizing their morphology and control together. However, this co-optimization of body and control is challenging, because controllers need some time to adapt to the evolving morphology - which may make it difficult for new and promising designs to enter the evolving population. A solution to this is to add intra-life learning, defined as an additional controller optimization loop, to each individual in the evolving population. A related problem is the lack of diversity often seen in evolving populations as evolution narrows the search down to a few promising designs too quickly. This problem can be mitigated by implementing full generational replacement, where offspring robots replace the whole population. This solution for increasing diversity usually comes at the cost of lower performance compared to using elitism. In this work, we show that combining such generational replacement with intra-life learning can increase diversity while retaining performance. We also highlight the importance of performance metrics when studying learning in morphologically evolving robots, showing that evaluating according to function evaluations versus according to generations of evolution can give different conclusions.

• Milojevic, Andrija & Glette, Kyrre (2025). Design and fabrication of soft locomotion robots based on spatial compliant mechanisms. Smart materials and structures (Print). ISSN 0964-1726. 34(7). doi: 10.1088/1361-665x/addf98. Full text in Research Archive Show summary

  Soft robotics has emerged as a promising technology that holds great potential for various application areas. This is due to soft materials unique properties, including flexibility, safety, and shock absorption, among others. Despite many advancement in the field, the development of effective design methodologies and production techniques for soft robots remains a challenge. Although numerous robot prototypes have been proposed in recent years, their designs are often complex and difficult to produce. As such, there is a need for more efficient and unified design approaches that can facilitate the production of soft robots with desirable properties. In this paper, we propose a method for designing soft robots using elastic beams and spatial compliant mechanisms. The method is based on an evolutionary approach that enables the creation of designs with both high motion and force transmission ratios. Specifically, we focus on the development of locomotion mechanisms using a central linear actuator. Our approach involves the use of commonly available plastic materials and a 3D printer to manufacture the designs. We demonstrate the feasibility of our approach by presenting experimental results that show successful production and real-world operation. Overall, our findings suggest that the use of elastic beams and an evolutionary approach can facilitate the creation of soft robots with desirable locomotion properties, including fast locomotion up to 3.7 body lengths per second, locomotion with a payload, and underwater locomotion. This method has the potential to enable the development of more efficient and practical soft robots for various applications.

• Christodoulou, Anna-Maria; Glette, Kyrre; Lartillot, Olivier & Jensenius, Alexander Refsum (2025). MusiQAl: A Dataset for Music Question–Answering through Audio–Video Fusion. Transactions of the International Society for Music Information Retrieval. 8(1), p. 265–282. doi: 10.5334/tismir.222. Full text in Research Archive Show summary

  Music question–answering (MQA) is a machine learning task where a computational system analyzes and answers questions about music‑related data. Traditional methods prioritize audio, overlooking visual and embodied aspects crucial to music performance understanding. We introduce MusiQAl, a multimodal dataset of 310 music performance videos and 11,793 human‑annotated question–answer pairs, spanning diverse musical traditions and styles. Grounded in musicology and music psychology, MusiQAl emphasizes multimodal reasoning, causal inference, and cross‑cultural understanding of performer–music interaction. We benchmark AVST and LAVISH architectures on MusiQAI, revealing strengths and limitations, underscoring the importance of integrating multimodal learning and domain expertise to advance MQA and music information retrieval.

• Wallace, Benedikte; Glette, Kyrre & Szorkovszky, Alexander (2025). How can we make robot dance expressive and responsive? A survey of methods and future directions. Frontiers in Computer Science. 7. doi: 10.3389/fcomp.2025.1575667. Full text in Research Archive Show summary

  The development of robots that can dance like humans presents a complex challenge due to the disparate abilities involved and various aesthetic qualities that need to be achieved. This article reviews recent advances in robotics, artificial intelligence, and human-robot interaction toward enabling various aspects of realistic dance, and examines potential paths toward a fully embodied dancing agent. We begin by outlining the essential abilities required for a robot to perform human-like dance movements and the resulting aesthetic qualities, summarized under the terms expressiveness and responsiveness . Subsequently, we present a review of the current state-of-the-art in dance-related robot technology, highlighting notable achievements, limitations and trade-offs in existing systems. Our analysis covers various approaches, including traditional control systems, machine learning algorithms, and hybrid systems that aim to imbue robots with the capacity for responsive, expressive movement. Finally, we identify and discuss the critical gaps in current research and technology that need to be addressed for the full realization of realistic dancing robots. These include challenges in real-time motion planning, adaptive learning from human dancers, and morphology independence. By mapping out current methods and challenges, we aim to provide insights that may guide future innovations in creating more engaging, responsive, and expressive robotic systems.

• Norstein, Emma Stensby; Yasui, Kotaro; Kano, Takeshi; Ishiguro, Akio & Glette, Kyrre (2025). Behaviour Diversity in a Walking and Climbing Centipede-Like Virtual Creature. Artificial Life. ISSN 1064-5462. 31(3), p. 321–344. doi: 10.1162/artl_a_00476. Full text in Research Archive Show summary

  Robot controllers are often optimized for a single robot in a single environment. This approach proves brittle, as such a controller will often fail to produce sensible behavior for a new morphology or environment. In comparison, animal gaits are robust and versatile. By observing animals, and attempting to extract general principles of locomotion from their movement, we aim to design a single, decentralized controller applicable to diverse morphologies and environments. The controller implements the three components of (a) undulation, (b) peristalsis, and (c) leg motion, which we believe are the essential elements in most animal gaits. This work is a first step toward a general controller. Accordingly, the controller has been evaluated on a limited range of simulated centipede-like robot morphologies. The centipede is chosen as inspiration because it moves using both body contractions and legged locomotion. For a controller to work in qualitatively different settings, it must also be able to exhibit qualitatively different behaviors. We find that six different modes of locomotion emerge from our controller in response to environmental and morphological changes. We also find that different parts of the centipede model can exhibit different modes of locomotion, simultaneously, based on local morphological features. This controller can potentially aid in the design or evolution of robots, by quickly testing the potential of a morphology, or be used to get insights about underlying locomotion principles in the centipede.

• Bravo, Pedro Pablo Lucas; Fasciani, Stefano; Szorkovszky, Alexander & Glette, Kyrre (2025). An Interactive Self-Assembly Swarm Music System in Extended Reality. In Seiça, Mariana & Wirfs-Brock, Jordan (Ed.), AM '25: Proceedings of the 20th International Audio Mostly Conference. Association for Computing Machinery (ACM). ISSN 9798400720659. p. 255–269. doi: 10.1145/3771594.3771620. Full text in Research Archive Show summary

  This paper explores the music-making capabilities of a swarm intelligence type of algorithm known as self-assembly in an interactive context using Extended Reality (XR) technologies. We describe the modifications made to a fully autonomous version of this algorithm, which we proposed in a previous work, allowing us to adapt it for user-interactive music. Moreover, we present the design of an XR system that supports this adaptation, modelled as a human-swarm interactive music system, which is implemented in the Meta Quest 3 headset. An auto-ethnographic study was conducted to discover the affordances of the system in a music improvisation session. The study, supported by empirical measurements collected during the session, enables a comparison between the interactive version and the original autonomous offline version, providing valuable insights into how a user can influence the swarm's behaviour. The results are used to discuss the music performance possibilities and future directions for this type of interactive music system.

• Fuhrer, Julian; Glette, Kyrre; Ivanovic, Jugoslav; Larsson, Pål Gunnar; Bekinschtein, Tristan & Kochen, Silvia [Show all 11 contributors for this article] (2025). Direct brain recordings reveal implicit encoding of structure in random auditory streams. Scientific Reports. 15. doi: 10.1038/s41598-025-98865-5. Full text in Research Archive Show summary

  The brain excels at processing sensory input, even in rich or chaotic environments. Mounting evidence attributes this to sophisticated internal models of the environment that draw on statistical structures in the unfolding sensory input. Understanding how and where such modeling proceeds is a core question in statistical learning and predictive processing. In this context, we address the role of transitional probabilities as an implicit structure supporting the encoding of the temporal structure of a random auditory stream. Leveraging information-theoretical principles and the high spatiotemporal resolution of intracranial electroencephalography, we analyzed the trial-by-trial high-frequency activity representation of transitional probabilities. This unique approach enabled us to demonstrate how the brain automatically and continuously encodes structure in random stimuli and revealed the involvement of a network outside of the auditory system, including hippocampal, frontal, and temporal regions. Our work provides a comprehensive picture of the neural correlates of automatic encoding of implicit structure that can be the crucial substrate for the swift detection of patterns and unexpected events in the environment.

• Bravo, Pedro Pablo Lucas; Szorkovszky, Alexander; Fasciani, Stefano & Glette, Kyrre (2024). Self-Assembly and Synchronization: Crafting Music with Multi-Agent Embodied Oscillators. In IEEE, IEEE (Eds.), 2024 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350363876. p. 145–150. doi: 10.1109/ACSOS61780.2024.00034. Full text in Research Archive Show summary

  This paper proposes a self-assembly algorithm that generates rhythmic music. It uses multiple pulsed oscillators embedded in cube-shaped agents in a virtual 3D space. When these units connect with each other, their oscillators synchronize, triggering regular sound events that produce musical notes whose sound dynamics change based on the size of the structures formed. This study examines the synchronization time of these oscillators and the emergent properties of the structures formed during the algorithm’s execution. Moreover, the resulting sound, determined by multiple interactions among agents, is analyzed in the time and frequency domains from its signal. The results show that the synchronization time slightly increases when more agents participate, although with high variability. Also, a quasi-regular pattern of increase and decrease in the number of structures over time is observed. Additionally, the signal analysis illustrates the effect of the self-assembly strategy in terms of rhythmical patterns and sound energy over time. We discuss these results and the potential applications of this multi-agent approach in the sound and music field.

• Guo, Jinyue; Christodoulou, Anna-Maria; Laczko, Balint & Glette, Kyrre (2024). LVNS-RAVE: Diversified audio generation with RAVE and Latent Vector Novelty Search. In Li, Xiaodong & Handl, Julia (Ed.), GECCO '24 Companion: Proceedings of the Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery (ACM). ISSN 9798400704956. p. 667–670. doi: 10.1145/3638530.3654432. Full text in Research Archive Show summary

  Evolutionary Algorithms and Generative Deep Learning have been two of the most powerful tools for sound generation tasks. However, they have limitations: Evolutionary Algorithms require complicated designs, posing challenges in control and achieving realistic sound generation. Generative Deep Learning models often copy from the dataset and lack creativity. In this paper, we propose LVNS-RAVE, a method to combine Evolutionary Algorithms and Generative Deep Learning to produce realistic and novel sounds. We use the RAVE model as the sound generator and the VGGish model as a novelty evaluator in the Latent Vector Novelty Search (LVNS) algorithm. The reported experiments show that the method can successfully generate diversified, novel audio samples under different mutation setups using different pre-trained RAVE models. The characteristics of the generation process can be easily controlled with the mutation parameters. The proposed algorithm can be a creative tool for sound artists and musicians.

• Christodoulou, Anna-Maria; Dutta, Sagar; Lartillot, Olivier Serge Gabriel; Glette, Kyrre & Jensenius, Alexander Refsum (2024). Exploring Convolutional Neural Network Models for Multimodal Classification of Expressive Piano Performance, Proceedings of the Sound and Music Computing Conference 2024. SMC Network. ISSN 9789893520758. Full text in Research Archive Show summary

  This paper addresses improving performance analysis by automating the recognition of expressive performance styles. We propose a multimodal fusion approach integrating audio, video, and motion data. We demonstrate the effectiveness of our approach by utilizing convolutional neural network (CNN) models. Training is done on a classical piano dataset of 211 excerpts containing audio, video, MIDI, and motion capture data. The results highlight the robustness of the CNN models; they achieve high accuracy even when trained on a limited dataset. Our study contributes to advancing the field of performance analysis by applying deep learning techniques to multimodal data.

• Bravo, Pedro Pablo Lucas; Fasciani, Stefano & Glette, Kyrre (2024). Csound vs. ChucK: Sound Generation for XR Multi-Agent Audio Systems in the Meta Quest 3 Using the Unity Game Engine, Proceedings of the Sound and Music Computing Conference 2024. SMC Network. ISSN 9789893520758. doi: 10.5281/zenodo.14339046. Full text in Research Archive Show summary

  Extended Reality (XR) technologies, particularly headsets like the Meta Quest 3, are revolutionizing the field of immersive sound and music applications by offering new depths of user experience. As such, the Unity game engine emerges as a preferred platform for building such auditory environments. As part of its capabilities, Unity allows the programming of sound generation through a low-level digital signal processing API, which requires specialized knowledge and significant effort for development. However, wrappers that integrate Unity with programming languages for sound synthesis can facilitate the implementation of this task. In this work, we focus on applications for the Meta Quest 3 involving multiple spatialized audio sources; such applications can be framed as XR multi-agent audio systems. We consider two wrappers, CsoundUnity and Chunity, featuring Csound and ChucK programming languages. We test and analyze these wrappers in a minimal XR application, varying the number of audio sources to measure the performance of both tools in two device environments: the development machine and the Meta Quest 3. We found that CsoundUnity performs better in the headset, but Chunity performs better in the development machine. We discuss the advantages, limitations, and computational issues found on both wrappers, as well as the criteria for choosing them to develop XR multi-agent audio applications in Unity.

• Bravo, Pedro Pablo Lucas; Fasciani, Stefano; Szorkovszky, Alexander & Glette, Kyrre (2024). Interactive Sonification of 3D Swarmalators, Proceedings of the International Conference on New Interfaces for Musical Expression. NIME. p. 252–260. doi: 10.5281/zenodo.13904846. Full text in Research Archive Show summary

  This paper explores the sound and music possibilities obtained from the sonification of a swarm of coupled oscillators moving in a virtual space called “Swarmalators”. We describe the design and implementation of a Human-Swarm Interactive Music System based on the 3D version of the Swarmalator model, which is used for signal analysis of the overall sound output in terms of scalability; that is, the effect of varying the number of agents in a swarm system. We also study the behaviour of autonomous swarmalators in the presence of one user-controlled agent, which we call the interactive swarmalator. We observed that sound frequencies barely deviate from their initial values when there are few agents, but they diverge significantly in a highly dense swarm. Additionally, with the inclusion of the interactive swarmalator, the group’s behaviour tends to adjust towards it. We use these results to explore the potential of swarmalators in music performance under various scenarios. Finally, we discuss opportunities and challenges to use the Swarmalator model for sound and music systems.

• Jónsson, Björn Thor; Erdem, Cagri; Fasciani, Stefano & Glette, Kyrre (2024). Cultivating Open-Earedness with Sound Objects discovered by Open-Ended Evolutionary Systems. In Grace, Kazjon; Llano, Maria Teresa; Martins, Pedro & Hedblom, Maria (Ed.), Proceedings of the Fifteenth International Conference on Computational Creativity. Association for Computational Creativity (ACC). ISSN 9789895416066. p. 381–386. Full text in Research Archive Show summary

  Interaction with generative systems can face the choice of generalising towards a middle ground or diverging towards novelty. Efforts have been made in the domain of sounds to enable divergent exploration in search of interesting discoveries. Those efforts have been confined by pre-trained models and single environments. We are building on those efforts to enable autonomous discovery of sonic landscapes. Furthermore, we draw inspiration from research on open-ended evolution to continuously provide evolutionary processes with new opportunities for sonic discoveries. Exposure to autonomously discovered sound objects can elevate openness to sonic experiences, which in turn offers inspiring opportunities for creative work involving sounds.

• Jónsson, Björn Thor; Erdem, Cagri & Glette, Kyrre (2024). A System for Sonic Explorations With Evolutionary Algorithms. Journal of The Audio Engineering Society. ISSN 1549-4950. 72(4), p. 257–266. doi: 10.17743/jaes.2022.0137. Full text in Research Archive
• Jónsson, Björn Thor; Erdem, Cagri; Fasciani, Stefano & Glette, Kyrre (2024). Towards Sound Innovation Engines Using Pattern-Producing Networks and Audio Graphs. In Johnson, Colin; Rebelo, Sérgio M. & Santos, Iria (Ed.), Artificial Intelligence in Music, Sound, Art and Design. Springer Nature. ISSN 9783031569920. doi: 10.1007/978-3-031-56992-0_14. Full text in Research Archive Show summary

  This study draws on the challenges that composers and sound designers face in creating and refining new tools to achieve their musical goals. Utilising evolutionary processes to promote diversity and foster serendipitous discoveries, we propose to automate the search through uncharted sonic spaces for sound discovery. We argue that such diversity promoting algorithms can bridge a technological gap between the theoretical realisation and practical accessibility of sounds. Specifically, in this paper we describe a system for generative sound synthesis using a combination of Quality Diversity (QD) algorithms and a supervised discriminative model, inspired by the Innovation Engine algorithm. The study explores different configurations of the generative system and investigates the interplay between the chosen sound synthesis approach and the discriminative model. The results indicate that a combination of Compositional Pattern Producing Network (CPPN) + Digital Signal Processing (DSP) graphs coupled with Multi-dimensional Archive of Phenotypic Elites (MAP-Elites) and a deep learning classifier can generate a substantial variety of synthetic sounds. The study concludes by presenting the generated sound objects through an online explorer and as rendered sound files. Furthermore, in the context of music composition, we present an experimental application that showcases the creative potential of our discovered sounds.

• Veenstra, Frank; Szorkovszky, Alexander & Glette, Kyrre (2023). Decentralized Control and Morphological Evolution of 2D Virtual Creatures. In Iizuki, Hiroyuki; Suzuki, Keisuke; Uno, Ryoko; Damiano, Luisa; Spychala, Nadine; Aguilera, Miguel; Izquierdo, Eduardo; Suzuki, Reiji & Baltieri, Manuel (Ed.), ALIFE 2023: Ghost in the Machine: Proceedings of the 2023 Artificial Life Conference. MIT Press. doi: 10.1162/isal_a_00656. Full text in Research Archive
• Szorkovszky, Alexander; Veenstra, Frank & Glette, Kyrre (2023). From real-time adaptation to social learning in robot ecosystems. Frontiers in Robotics and AI. 10. doi: 10.3389/frobt.2023.1232708. Full text in Research Archive Show summary

  While evolutionary robotics can create novel morphologies and controllers that are well-adapted to their environments, learning is still the most efficient way to adapt to changes that occur on shorter time scales. Learning proposals for evolving robots to date have focused on new individuals either learning a controller from scratch, or building on the experience of direct ancestors and/or robots with similar configurations. Here we propose and demonstrate a novel means for social learning of gait patterns, based on sensorimotor synchronization. Using movement patterns of other robots as input can drive nonlinear decentralized controllers such as CPGs into new limit cycles, hence encouraging diversity of movement patterns. Stable autonomous controllers can then be locked in, which we demonstrate using a quasi-Hebbian feedback scheme. We propose that in an ecosystem of robots evolving in a heterogeneous environment, such a scheme may allow for the emergence of generalist task-solvers from a population of specialists.

• Szorkovszky, Alexander; Veenstra, Frank & Glette, Kyrre (2023). Central pattern generators evolved for real-time adaptation to rhythmic stimuli. Bioinspiration & Biomimetics. ISSN 1748-3182. 18(4). doi: 10.1088/1748-3190/ace017. Full text in Research Archive Show summary

  Abstract For a robot to be both autonomous and collaborative requires the ability to adapt its movement to a variety of external stimuli, whether these come from humans or other robots. Typically, legged robots have oscillation periods explicitly defined as a control parameter, limiting the adaptability of walking gaits. Here we demonstrate a virtual quadruped robot employing a bio-inspired central pattern generator (CPG) that can spontaneously synchronize its movement to a range of rhythmic stimuli. Multi-objective evolutionary algorithms were used to optimize the variation of movement speed and direction as a function of the brain stem drive and the centre of mass control respectively. This was followed by optimization of an additional layer of neurons that filters fluctuating inputs. As a result, a range of CPGs were able to adjust their gait pattern and/or frequency to match the input period. We show how this can be used to facilitate coordinated movement despite differences in morphology, as well as to learn new movement patterns.

• Fuhrer, Julian; Glette, Kyrre; Anais, Llorens; Endestad, Tor; Solbakk, Anne-Kristin & Blenkmann, Alejandro Omar (2023). Quantifying evoked responses through information-theoretical measures. Frontiers in Neuroinformatics. 17. doi: 10.3389/fninf.2023.1128866. Full text in Research Archive Show summary

  Information theory is a viable candidate to advance our understanding of how the brain processes information generated in the internal or external environment. With its universal applicability, information theory enables the analysis of complex data sets, is free of requirements about the data structure, and can help infer the underlying brain mechanisms. Information-theoretical metrics such as Entropy or Mutual Information have been highly beneficial for analyzing neurophysiological recordings. However, a direct comparison of the performance of these methods with well-established metrics, such as the t- test, is rare. Here, such a comparison is carried out by evaluating the novel method of Encoded Information with Mutual Information, Gaussian Copula Mutual Information, Neural Frequency Tagging, and t -test. We do so by applying each method to event-related potentials and event-related activity in different frequency bands originating from intracranial electroencephalography recordings of humans and marmoset monkeys. Encoded Information is a novel procedure that assesses the similarity of brain responses across experimental conditions by compressing the respective signals. Such an information-based encoding is attractive whenever one is interested in detecting where in the brain condition effects are present.

• Szorkovszky, Alexander; Veenstra, Frank; Lartillot, Olivier Serge Gabriel; Jensenius, Alexander Refsum & Glette, Kyrre (2023). Embodied Tempo Tracking with a Virtual Quadruped, Proceedings of the Sound and Music Computing Conference 2023. SMC Network . ISSN 9789152773727. doi: 10.5281/zenodo.10060970. Full text in Research Archive Show summary

  Dynamic attending theory posits that we entrain to time-structured events in a similar way to synchronizing oscillators. Hence, a tempo tracker based on oscillators may replicate humans' ability to rapidly and robustly identify musical tempi. We demonstrate this idea using virtual quadrupeds, whose gaits are controlled by oscillatory neural circuits known as central pattern generators (CPGs). The quadruped CPGs were first optimized for flexible gait frequency and direction, and then an additional recurrent layer was optimized for entrainment to isochronous pulses. Using excerpts of musical pieces, we find that the motion of these agents can rapidly entrain to simple rhythms. Performance was found to be partially predicted by pulse entropy, a measure of the sample's rhythmic complexity. Notably, in addition to having wide tempo ranges, the best performing agents can also entrain to rhythms that are periodic but not quantized on a grid. Our approach offers an embodied alternative to other dynamical systems-based approaches to entrainment, such as gradient-frequency arrays. Such agents could find use as participants in virtual musicking environments, or as real-world musical robots.

• Erdem, Cagri; Wallace, Benedikte; Glette, Kyrre & Jensenius, Alexander Refsum (2023). Tool or Actor? Expert Improvisers’ Evaluation of a Musical AI “Toddler”. Computer Music Journal. ISSN 0148-9267. 46(4), p. 26–42. doi: 10.1162/comj_a_00657. Full text in Research Archive Show summary

  Abstract In this article, we introduce the coadaptive audiovisual instrument, CAVI. This instrument uses deep learning to generate control signals based on muscle and motion data of a performer's actions. The generated signals control time-based live sound-processing modules. How does a performer perceive such an instrument? Does it feel like a machine learning–based musical tool? Or is it an actor with the potential to become a musical partner? We report on an evaluation of CAVI after it had been used in two public performances. The evaluation is based on interviews with the performers, audience questionnaires, and the creator's self-analysis. Our findings suggest that the perception of CAVI as a tool or actor correlates with the performer's sense of agency. The perceived agency changes throughout a performance based on several factors, including perceived musical coordination, the balance between surprise and familiarity, a “common sense,” and the physical characteristics of the performance setting.

• Norstein, Emma Stensby; Veenstra, Frank; Ellefsen, Kai Olav; Nygaard, Tønnes & Glette, Kyrre (2023). Effects of compliant and structural parts in evolved modular robots. In Iizuki, Hiroyuki; Suzuki, Keisuke; Uno, Ryoko; Damiano, Luisa; Spychala, Nadine; Aguilera, Miguel; Izquierdo, Eduardo; Suzuki, Reiji & Baltieri, Manuel (Ed.), ALIFE 2023: Ghost in the Machine: Proceedings of the 2023 Artificial Life Conference. MIT Press. doi: 10.1162/isal_a_00689. Full text in Research Archive Show summary

  A striking difference between animals and traditional robots is that the latter usually have rigid and non-flexible bodies. Animals, on the other hand, exhibit highly adapted traits, such as elastic tendons. The tendons work as springs, storing and releasing kinetic energy during an animal’s gait cycle. Springs have been used in some hand designed robots for similar benefits. However, little research has been done on springs in robots with evolving morphology. We examine the use of compliant and structural modules in modular robots, using a standard evolutionary algorithm. We also look at connections between spring stiffness and robot size using the quality diversity algorithm MAP-Elites. We found that the modular robots evolved to use elastic actuators, and that structural modules enabled morphologies that use less actuators, but still achieve the same walking speed as the robots with actuators in every module. We also observe some indications that larger robots may require lower elasticity.

• Norstein, Emma Stensby; Ellefsen, Kai Olav; Veenstra, Frank; Nygaard, Tønnes & Glette, Kyrre (2023). Evolution of linkages for prototyping of linkage based robots, Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence (SSCI 2022). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781665487689. p. 1283–1290. doi: 10.1109/SSCI51031.2022.10022197. Full text in Research Archive Show summary

  Prototyping robotic systems is a time consuming process. Computer aided design, however, might speed up the process significantly. Quality-diversity evolutionary approaches optimise for novelty as well as performance, and can be used to generate a repertoire of diverse designs. This design repertoire could be used as a tool to guide a designer and kick-start the rapid prototyping process. This paper explores this idea in the context of mechanical linkage based robots. These robots can be a good test-bed for rapid prototyping, as they can be modified quickly for swift iterations in design. We compare three evolutionary algorithms for optimising 2D mechanical linkages: 1) a standard evolutionary algorithm, 2) the multi-objective algorithm NSGA-II, and 3) the quality-diversity algorithm MAP-Elites. Some of the found linkages are then realized on a physical hexapod robot through a prototyping process, and tested on two different floors. We find that all the tested approaches, except the standard evolutionary algorithm, are capable of finding mechanical linkages that creates a path similar to a specified desired path. However, the quality-diversity approaches that had the length of the linkage as a behaviour descriptor were the most useful when prototyping. This was due to the quality-diversity approaches having a larger variety of similar designs to choose from, and because the search could be constrained by the behaviour descriptors to make linkages that were viable for construction on our hexapod platform.

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• Glette, Kyrre (2026). How can we use ALICE concepts for creative purposes? doi: https:/2026.aliceworkshop.org/speakers/. Full text in Research Archive
• Stensby, Emma Hjellbrekke; Yasui, Kotaro; Kano, Takeshi; Ishiguro, Akio & Glette, Kyrre (2026). Behaviour Diversity in a Walking and Climbing Centipede-Like Virtual Creature. Full text in Research Archive
• Ellefsen, Kai Olav; Bruin, Ege de; Glette, Kyrre & Luck, Kevin (2026). Special Session: Co-design of Robot Morphology and Control. doi: https:/www.mn.uio.no/ifi/english/people/aca/kaiolae/alife-2026-special-session-on-co-design-of-robot-m.html. Full text in Research Archive
• Veenstra, Frank; Bruin, Ege de; Glette, Kyrre & Ellefsen, Kai Olav (2025). Co-Optimization of Robot Bodies and Brains with Evolutionary Algorithms. Full text in Research Archive Show summary

  Read this story on the University of Oslo's website.

• Christodoulou, Anna-Maria; Glette, Kyrre; Lartillot, Olivier & Jensenius, Alexander Refsum (2025). MusiQAl: Music Question Answering through Audio-Video fusion. doi: https:/ismir2025.ismir.net/program-detailed-schedule. Full text in Research Archive
• Glette, Kyrre (2025). Biologically-inspired AI: A framework for designing adaptive robots. Full text in Research Archive
• Glette, Kyrre (2024). Evolutionary design of morphology and control for simulated and real-world robots. Full text in Research Archive
• Søyseth, Vegard; Bergflødt, Adrian Anderson; Glette, Kyrre; Watanabe, Shin & Otterdijk, Marieke van (2024). Utstilling av roboter på SENT @ Teknisk museum. Full text in Research Archive
• Jónsson, Björn Thór; Erdem, Çağri; Fasciani, Stefano & Glette, Kyrre (2024). Towards Sound Innovation Engines Using Pattern-Producing Networks and Audio Graphs. Full text in Research Archive Show summary

  This study draws on the challenges that composers and sound designers face in creating and refining new tools to achieve their musical goals. Utilising evolutionary processes to promote diversity and foster serendipitous discoveries, we propose to automate the search through uncharted sonic spaces for sound discovery. We argue that such diversity promoting algorithms can bridge a technological gap between the theoretical realisation and practical accessibility of sounds. Specifically, in this paper we describe a system for generative sound synthesis using a combination of Quality Diversity (QD) algorithms and a discriminative model, inspired by the Innovation Engine algorithm. The study explores different configurations of the generative system and investigates the interplay between the chosen sound synthesis approach and the discriminative model. The results indicate that a combination of Compositional Pattern Producing Network (CPPN) + Digital Signal Processing (DSP) graphs coupled with Multi-dimensional Archive of Phenotypic Elites (MAP-Elites) and a deep learning classifier can generate a substantial variety of synthetic sounds. The study concludes by presenting the generated sound objects through an online explorer and as rendered sound files. Furthermore, in the context of music composition, we present an experimental application that showcases the creative potential of our discovered sounds.

• Glette, Kyrre (2024). Bio-inspiration and divergent search algorithms for robotics and sound exploration. Full text in Research Archive
• Norstein, Emma Stensby; Yasui, Kotaro; Kano, Takeshi; Glette, Kyrre & Ishiguro, Akio (2024). A bio-inspired decentralized approach to multi-morphology control. Full text in Research Archive Show summary

  Traditional robot controllers are usually optimized for a specific robot design, and will often fail if the robot’s morphology is changed. This can be a challenge for robustness to damage, self-reconfiguring robots, or in the case of morphology design search algorithms, where each new robot design dictates re-learning the controller. Here, we take on a highly bio-inspired approach, taking inspiration from the versatility of myriapod locomotion, to create a multi-morphology controller. We propose a simple decentralized controller model, which can, without change in parameters, adapt to various centipede-like morphologies and display different behaviors based on changes in the morphology and environment. The approach shows potential for robot design and could potentially be useful for understanding mechanisms of animal locomotion.

• Glette, Kyrre; Ellefsen, Kai Olav; Norstein, Emma Stensby & Bruin, Ege de (2024). Automatic Design of Robot Bodies and Brains with Evolutionary Algorithms - Tutorial. Full text in Research Archive Show summary

  The evolution of robot bodies and brains allows researchers to investigate which building blocks are interesting for evolving Artificial Life, and how controllers and morphologies can be shaped together for automated robot design. This tutorial aims to introduce evolution of robot body and control, and some of the key challenges one faces when doing experiments in Evolutionary Robotics. These include finding good ways to represent robots (genotypic encodings), challenges related to co-optimizing morphology and control, how environments shape body and control, and selecting the right physical substrate for evolving robots. After introducing these challenges and showing relevant examples from our own and other labs’ research, we will present a short demo of how to run Evolutionary Robotics experiments in practice, with the Unity ML-Agents framework.

• Glette, Kyrre (2024). Evolution of morphology and control - from simulation to reality. Full text in Research Archive
• Jónsson, Björn Thór; Erdem, Cagri; Fasciani, Stefano & Glette, Kyrre (2024). Cultivating Open-Earedness with Sound Objects discovered by Open-Ended Evolutionary Systems. Full text in Research Archive Show summary

  Interaction with generative systems can face the choice of generalising towards a middle ground or diverging towards novelty. Efforts have been made in the domain of sounds to enable divergent exploration in search of interesting discoveries. Those efforts have been confined by pre-trained models and single environments. We are building on those efforts to enable autonomous discovery of sonic landscapes. Furthermore, we draw inspiration from research on open-ended evolution to continuously provide evolutionary processes with new opportunities for sonic discoveries. Exposure to autonomously discovered sound objects can elevate openness to sonic experiences, which in turn offers inspiring opportunities for creative work involving sounds.

• Christodoulou, Anna-Maria; Dutta, Sagar; Lartillot, Olivier; Glette, Kyrre & Jensenius, Alexander Refsum (2024). Exploring Convolutional Neural Network Models for Multimodal Classification of Expressive Piano Performance. Full text in Research Archive
• Kvalsund, Mia-Katrin; Ellefsen, Kai; Glette, Kyrre; Pontes-Filho, Sidney & Lepperød, Mikkel (2023). The distributed neocortex: How neuroscience can inspire distributed AI systems. Full text in Research Archive
• Szorkovszky, Alexander; Veenstra, Frank & Glette, Kyrre (2023). Toward cultures of rhythm in legged robots. doi: 10.1162/isal_a_00673. Full text in Research Archive Show summary

  It is widely thought that sensorimotor synchronization, underpinning cultural domains such as music and dance, played a critical role in the evolution of human sociality. Here, we present virtual legged robots controlled by central pattern generators (CPGs) that evolve to synchronize motion to rhythmic sensory input in real time. Multi-stage, multi-objective evolutionary algorithms were used to maximize flexibility of the CPGs with respect to control parameters, and then to optimize a neural input layer for wide-ranging susceptibility to rhythmic inputs. The evolved CPGs self-organize to accommodate the input sequence over a range of frequencies and patterns while keeping the agents upright. We show how this behaviour can be scaled up to multiple interacting agents, including with differing morphologies, to produce novel behaviours. We then outline how spike timing dependent plasticity can be used for the acquisition of new motor patterns. Finally, taking inspiration from biocultural evolution and cognitive neuroscience, we suggest ways in which real-time social adaptation can play a key role in the evolution of complex social behaviours in robots.

• Glette, Kyrre (2023). Adaptive robots through evolutionary algorithms and machine learning. Full text in Research Archive
• Glette, Kyrre (2023). Evolutionary and adaptive robotics: from simulation to reality. Full text in Research Archive
• Glette, Kyrre (2023). Bio-inspiration for robot design and adaptation. Full text in Research Archive
• Veenstra, Frank; Norstein, Emma Stensby & Glette, Kyrre (2023). Tutorial: Evolving Robot Bodies and Brains with Unity. Full text in Research Archive Show summary

  The evolution of robot bodies and brains allows researchers to investigate which building blocks are interesting for evolving artificial life. Agnostic to the evolutionary approach used, the supplied building blocks influence how artificial organisms will behave. What should these building blocks look like? How should we associate control units with these building blocks? How should we represent the genomes of these robots? In this tutorial we discuss (1) previous approaches to evolving robots and virtual creatures, (2) outline how Unity simulations and Unity's ML-agents package can be used as an interface, and (3) our approach to evolving bodies and brains using Unity. There are many existing solutions that are tailored to experimenting with body brain co-optimization and we have been using several simulation approaches to evolve modular robots that are represented by directed trees (directed acyclic graphs). Since evolving bodies can be relatively complex, we give participants an overview of existing methods and invite the participants to get some guided hands-on experience using the Unity ML-Agents for evolving robots. The Unity ML-Agents toolkit is an open-source toolkit for game developers, AI researchers, and hobbyists that can be used to train agents using various AI methods. Similar to OpenAI gym, it supplies a Python API through which one can optimize agents in a variety of environments. The Unity ML-Agents toolkit provides an easy-to-use interface that is flexible enough to allow for quick design iterations for evolving robot bodies and brains. This tutorial is aimed at researchers that are interested in simulating the evolution of bodies and brains of robots. The tutorial will provide an overview of existing approaches to evolving bodies and brains of robots, and demonstrate how to design and incorporate control units, morphological components, environments and objectives. Participants will learn how to use Unity ML-Agents as a tool with evolutionary algorithms and learn how they can create incorporate their own robotic modules for evolving robots.

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