Kyrre Glette

• 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. doi: 10.17743/jaes.2022.0137.
• Jónsson, Björn Thor; Erdem, Cagri; Fasciani, Stefano & Glette, Kyrre (2024). Towards Sound Innovation Engines Using Pattern-Producing Networks and Audio Graphs. I Johnson, Colin; Rebelo, Sérgio M. & Santos, Iria (Red.), Artificial Intelligence in Music, Sound, Art and Design. Springer Nature. ISSN 9783031569920. doi: 10.1007/978-3-031-56992-0_14. Fulltekst i vitenarkiv
• Norstein, Emma Stensby; Veenstra, Frank; Ellefsen, Kai Olav; Nygaard, Tønnes & Glette, Kyrre (2023). Effects of compliant and structural parts in evolved modular robots. I Iizuki, Hiroyuki; Suzuki, Keisuke; Uno, Ryoko; Damiano, Luisa; Spychala, Nadine; Aguilera, Miguel; Izquierdo, Eduardo; Suzuki, Reiji & Baltieri, Manuel (Red.), ALIFE 2023: Ghost in the Machine: Proceedings of the 2023 Artificial Life Conference. MIT Press. ISSN 0000000000000. doi: 10.1162/isal_a_00689. Fulltekst i vitenarkiv
• Szorkovszky, Alexander; Veenstra, Frank & Glette, Kyrre (2023). Toward cultures of rhythm in legged robots. I Iizuki, Hiroyuki; Suzuki, Keisuke; Uno, Ryoko; Damiano, Luisa; Spychala, Nadine; Aguilera, Miguel; Izquierdo, Eduardo; Suzuki, Reiji & Baltieri, Manuel (Red.), ALIFE 2023: Ghost in the Machine: Proceedings of the 2023 Artificial Life Conference. MIT Press. ISSN 0000000000000. doi: https:/doi.org/10.1162/isal_a_00673. Fulltekst i vitenarkiv
• Veenstra, Frank; Szorkovszky, Alexander & Glette, Kyrre (2023). Decentralized Control and Morphological Evolution of 2D Virtual Creatures . I Iizuki, Hiroyuki; Suzuki, Keisuke; Uno, Ryoko; Damiano, Luisa; Spychala, Nadine; Aguilera, Miguel; Izquierdo, Eduardo; Suzuki, Reiji & Baltieri, Manuel (Red.), ALIFE 2023: Ghost in the Machine: Proceedings of the 2023 Artificial Life Conference. MIT Press. ISSN 0000000000000. doi: https:/doi.org/10.1162/isal_a_00656.
• Lucas Bravo, Pedro Pablo & Glette, Kyrre (2023). Human-Swarm Interactive Music Systems: Design, Algorithms, Technologies, and Evaluation. I Kitahara, Tetsuro; Aramaki, Mitsuko; Kronland-Martinet, Richard & Ystad, Sølvi (Red.), Proceedings of the 16th International Symposium on Computer Music Multidisciplinary Research. Zenodo. ISSN 979-10-97498-04-7. s. 347–358. doi: https:/doi.org/10.5281/zenodo.10113081. Vis sammendrag

  This paper presents considerations for developing Human-Swarm Interactive Music Systems (IMS), based on previous work in the field. We discuss design principles, algorithms, technologies, and evaluation methods for creating user-centered Human-Swarm IMSs using architectural ap­proaches, swarm strategies, and levels of embodiment in implementation. Our contribution aims to establish a framework for future applications and research studies on swarm-based music platforms.

• 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. doi: 10.1162/comj_a_00657.
• 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 978-91-527-7372-7. doi: 10.5281/zenodo.10060970. Fulltekst i vitenarkiv
• Szorkovszky, Alexander; Veenstra, Frank & Glette, Kyrre (2023). From real-time adaptation to social learning in robot ecosystems. Frontiers in Robotics and AI. ISSN 2296-9144. 10. doi: 10.3389/frobt.2023.1232708. Fulltekst i vitenarkiv
• 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. Fulltekst i vitenarkiv
• 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. ISSN 1662-5196. 17. doi: 10.3389/fninf.2023.1128866. Fulltekst i vitenarkiv
• 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 conference proceedings. ISSN 978-1-6654-8768-9. s. 1283–1290. doi: 10.1109/SSCI51031.2022.10022197. Fulltekst i vitenarkiv
• Torpmann-Hagen, Birk Sebastian Frostelid; Thambawita, Vajira L B; Riegler, Michael; Halvorsen, Pål & Glette, Kyrre (2022). Segmentation Consistency Training: Out-of-Distribution Generalization for Medical Image Segmentation . I Bulterman, Dick; Kankanhalli, Mohan S.; Muehlhaeuser, Max; Persia, Fabio; Sheu, Phillip; Tsai, Jeffrey J.P.; D’Auria, Daniela; Ge, Mouzhi; Sert, Mustafa; Swaminathan, Vishy & Yamasaki, Toshihiko (Red.), Proceedings of the 2022 IEEE International Symposium on Multimedia (ISM). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-6654-7172-5. s. 42–49. doi: 10.1109/ISM55400.2022.00012.
• (2022). Benefits and Challenges of Morphological Adaptation in Real-world Robots. IOP Conference Series: Materials Science and Engineering. ISSN 1757-8981. 1261. doi: 10.1088/1757-899X/1261/1/012017. Fulltekst i vitenarkiv
• Kvalsund, Mia-Katrin Ose; Glette, Kyrre & Veenstra, Frank (2022). Centralized and Decentralized Control in Modular Robots and Their Effect on Morphology. I Holler, Silvia; Löffler, Richard & Bartlett, Stuart (Red.), ALIFE 2022: The 2022 Conference on Artificial Life. MIT Press. ISSN 0000000000000. doi: 10.1162/isal_a_00533. Vis sammendrag

  In Evolutionary Robotics, evolutionary algorithms are used to co-optimize morphology and control. However, co-optimizing leads to different challenges: How do you optimize a controller for a body that often changes its number of inputs and outputs? Researchers must then make some choice between centralized or decentralized control. In this article, we study the effects of centralized and decentralized controllers on modular robot performance and morphologies. This is done by implementing one centralized and two decentralized continuous time recurrent neural network controllers, as well as a sine wave controller for a baseline. We found that a decentralized approach that was more independent of morphology size performed significantly better than the other approaches. It also worked well in a larger variety of morphology sizes. In addition, we highlighted the difficulties of implementing centralized control for a changing morphology, and saw that our centralized controller struggled more with early convergence than the other approaches. Our findings indicate that duplicated decentralized networks are beneficial when evolving both the morphology and control of modular robots. Overall, if these findings translate to other robot systems, our results and issues encountered can help future researchers make a choice of control method when co-optimizing morphology and control.

• Veenstra, Frank; Olsen, Martin Herring & Glette, Kyrre (2022). Effects of Encodings and Quality-diversity on Evolving 2D Virtual Creatures, GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery (ACM). ISSN 9781450392686. s. 164–167. doi: https:/doi.org/10.1145/3520304.3529053.
• Szorkovszky, Alexander; Veenstra, Frank & Glette, Kyrre (2022). Rapid rhythmic entrainment in bio-inspired central pattern generators, 2022 International Joint Conference on Neural Networks (IJCNN). IEEE conference proceedings. ISSN 978-1-7281-8671-9. doi: 10.1109/IJCNN55064.2022.9891909. Fulltekst i vitenarkiv
• Krzyzaniak, Michael; Erdem, Cagri & Glette, Kyrre (2022). What Makes Interactive Art Engaging? Frontiers in Computer Science. ISSN 2624-9898. 4. doi: 10.3389/fcomp.2022.859496. Vis sammendrag

  Interactive art requires people to engage with it, and some works of interactive art are more intrinsically engaging than others. This article asks what properties of a work of interactive art promote engagement. More specifically, it examines four properties: (1) the number of controllable parameters in the interaction, (2) the use of fantasy in the work, (3) the timescale on which the work responds, and (4) the amount agency ascribed to the work. Each of these is hypothesized to promote engagement, and each hypothesis is tested with a controlled user study in an ecologically valid setting on the Internet. In these studies, we found that more controllable parameters increases engagement; the use of fantasy increases engagement for some users and not others; the timescale surprisingly has no significant on engagement but may relate to the style of interaction; and more ascribed agency is correlated with greater engagement although the direction of causation is not known. This is not intended to be an exhaustive list of all properties that may promote engagement, but rather a starting point for more studies of this kind.

• Fuhrer, Julian; Blenkmann, Alejandro Omar; Endestad, Tor; Solbakk, Anne-Kristin & Glette, Kyrre (2022). Complexity-based Encoded Information Quantification in Neurophysiological Recordings. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). ISSN 2694-0604. s. 2319–2323. doi: 10.1109/EMBC48229.2022.9871501. Vis sammendrag

  Brain activity differs vastly between sleep, cognitive tasks, and action. Information theory is an appropriate concept to analytically quantify these brain states. Based on neurophysiological recordings, this concept can handle complex data sets, is free of any requirements about the data structure, and can infer the present underlying brain mechanisms. Specifically, by utilizing algorithmic information theory, it is possible to estimate the absolute information contained in brain responses. While current approaches that apply this theory to neurophysiological recordings can discriminate between different brain states, they are limited in directly quantifying the degree of similarity or encoded information between brain responses. Here, we propose a method grounded in algorithmic information theory that affords direct statements about responses' similarity by estimating the encoded information through a compression-based scheme. We validated this method by applying it to both synthetic and real neurophysiological data and compared its efficiency to the mutual information measure. This proposed procedure is especially suited for task paradigms contrasting different event types because it can precisely quantify the similarity of neuronal responses.

• Norstein, Emma Stensby; Ellefsen, Kai Olav & Glette, Kyrre (2022). Open-Ended Search for Environments and Adapted Agents Using MAP-Elites, Applications of Evolutionary Computation. EvoApplications 2022.. Springer. ISSN 978-3-031-02461-0. s. 651–666. doi: 10.1007/978-3-031-02462-7_41.
• Potel, Pierre; Veenstra, Frank & Glette, Kyrre (2021). SoloJam Island : a platform for rhythmic experimentation in spatially segmented multi-agent systems. I Kantan, Prithvi Ravi; Paisa, Razvan & Willemsen, Silvin (Red.), Proceedings of the 2nd Nordic Sound and Music Computing (NordicSMC) Conference. Aalborg Universitetsforlag. doi: 10.5281/zenodo.5717860. Fulltekst i vitenarkiv
• (2021). Forskningsfeltet robotikk og intelligente systemer. Nytt Norsk Tidsskrift. ISSN 0800-336X. 38(4), s. 326–330. doi: 10.18261/issn.1504-3053-2021-04-07.
• Nordmoen, Jørgen; Nygaard, Tønnes Frostad; Samuelsen, Eivind & Glette, Kyrre (2021). On Restricting Real-Valued Genotypes in Evolutionary Algorithms. I Castillo, Pedro A. & Jiménez Laredo, Juan Luiz (Red.), Applications of Evolutionary Computation. Springer. ISSN 978-3-030-72699-7. s. 3–16. doi: 10.1007/978-3-030-72699-7_1. Fulltekst i vitenarkiv
• Barnes, Chloe M; Ekárt, Anikó; Ellefsen, Kai Olav; Glette, Kyrre; Lewis, Peter R. & Tørresen, Jim (2021). Evolving Neuromodulated Controllers in Variable Environments, 2021 IEEE International Conference on Autonomic Computing and Self-Organizing Systems . IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-6654-1261-2. s. 164–169. doi: 10.1109/ACSOS52086.2021.00037. Fulltekst i vitenarkiv
• Norstein, Emma Stensby; Ellefsen, Kai Olav & Glette, Kyrre (2021). Co-optimising Robot Morphology and Controller in a Simulated Open-Ended Environment. I Castillo, Pedro A. & Jiménez Laredo, Juan Luiz (Red.), Applications of Evolutionary Computation. Springer. ISSN 978-3-030-72699-7. doi: https:/doi.org/10.1007/978-3-030-72699-7_3. Fulltekst i vitenarkiv
• Barnes, Chloe M; Ekárt, Anikó; Ellefsen, Kai Olav; Glette, Kyrre; Lewis, Peter R. & Tørresen, Jim (2021). Behavioural Plasticity Can Help Evolving Agents in Dynamic Environments but at the Cost of Volatility. ACM Transactions on Autonomous and Adaptive Systems. ISSN 1556-4665. 15(4). doi: 10.1145/3487918. Fulltekst i vitenarkiv
• Ahmadi, Arash; Till, Katharina; Backe, Paul Hoff; Blicher, Pernille; Diekmann, Robin & Schüttpelz, Mark [Vis alle 10 forfattere av denne artikkelen] (2021). Non-flipping DNA glycosylase AlkD scans DNA without formation of a stable interrogation complex. Communications Biology. ISSN 2399-3642. 4. doi: 10.1038/s42003-021-02400-x. Fulltekst i vitenarkiv
• Nordmoen, Jørgen Halvorsen; Veenstra, Frank; Ellefsen, Kai Olav & Glette, Kyrre (2021). MAP-Elites Enables Powerful Stepping Stones and Diversity for Modular Robotics. Frontiers in Robotics and AI. ISSN 2296-9144. 8. doi: 10.3389/frobt.2021.639173. Fulltekst i vitenarkiv Vis sammendrag

  In modular robotics modules can be reconfigured to change the morphology of the robot, making it able to adapt to specific tasks. However, optimizing both the body and control of such robots is a difficult challenge due to the intricate relationship between fine-tuning control and morphological changes that can invalidate such optimizations. These challenges can trap many optimization algorithms in local optima, halting progress towards better solutions. To solve this challenge we compare three different Evolutionary Algorithms on their capacity to optimize high performing and diverse morphologies and controllers in modular robotics. We compare two objective-based search algorithms, with and without a diversity promoting objective, with a Quality Diversity algorithm—MAP-Elites. The results show that MAP-Elites is capable of evolving the highest performing solutions in addition to generating the largest morphological diversity. Further, MAP-Elites is superior at regaining performance when transferring the population to new and more difficult environments. By analyzing genealogical ancestry we show that MAP-Elites produces more diverse and higher performing stepping stones than the two other objective-based search algorithms. The experiments transitioning the populations to new environments show the utility of morphological diversity, while the analysis of stepping stones show a strong correlation between diversity of ancestry and maximum performance on the locomotion task. Together, these results demonstrate the suitability of MAP-elites for the challenging task of morphology-control search for modular robots, and shed light on the algorithm’s capability of generating stepping stones for reaching high-performing solutions.

• Nygaard, Tønnes; Martin, Charles Patrick; Howard, David; Tørresen, Jim & Glette, Kyrre (2021). Environmental Adaptation of Robot Morphology and Control Through Real-world Evolution. Evolutionary Computation. ISSN 1063-6560. doi: 10.1162/evco_a_00291. Fulltekst i vitenarkiv Vis sammendrag

  Robots operating in the real world will experience a range of different environments and tasks. It is essential for the robot to have the ability to adapt to its surroundings to work efficiently in changing conditions. Evolutionary robotics aims to solve this by optimizing both the control and body (morphology) of a robot, allowing adaptation to internal, as well as external factors. Most work in this field has been done in physics simulators, which are relatively simple and not able to replicate the richness of interactions found in the real world. Solutions that rely on the complex interplay between control, body, and environment are therefore rarely found. In this paper, we rely solely on real-world evaluations and apply evolutionary search to yield combinations of morphology and control for our mechanically self-reconfiguring quadruped robot. We evolve solutions on two distinct physical surfaces and analyze the results in terms of both control and morphology. We then transition to two previously unseen surfaces to demonstrate the generality of our method. We find that the evolutionary search finds high-performing and diverse morphology-controller configurations by adapting both control and body to the different properties of the physical environments. We additionally find that morphology and control vary with statistical significance between the environments. Moreover, we observe that our method allows for morphology and control parameters to transfer to previously-unseen terrains, demonstrating the generality of our approach.

• Nygaard, Tønnes; Martin, Charles Patrick; Tørresen, Jim; Glette, Kyrre & Howard, David (2021). Real-world embodied AI through a morphologically adaptive quadruped robot. Nature Machine Intelligence. doi: 10.1038/s42256-021-00320-3. Fulltekst i vitenarkiv Vis sammendrag

  Robots are traditionally bound by a fixed morphology during their operational lifetime, which is limited to adapting only their control strategies. Here we present the first quadrupedal robot that can morphologically adapt to different environmental conditions in outdoor, unstructured environments. Our solution is rooted in embodied AI and comprises two components: (1) a robot that permits in situ morphological adaptation and (2) an adaptation algorithm that transitions between the most energy-efficient morphologies on the basis of the currently sensed terrain. We first build a model that describes how the robot morphology affects performance on selected terrains. We then test continuous adaptation on realistic outdoor terrain while allowing the robot to constantly update its model. We show that the robot exploits its training to effectively transition between different morphological configurations, exhibiting substantial performance improvements over a non-adaptive approach. The demonstrated benefits of real-world morphological adaptation demonstrate the potential for a new embodied way of incorporating adaptation into future robotic designs.

• Côté-Allard, Ulysse; Gagnon-Turcotte, Gabriel; Phinyomark, Angkoon; Glette, Kyrre Harald; Scheme, Erik & Laviolette, François (2021). A Transferable Adaptive Domain Adversarial Neural Network for Virtual Reality Augmented EMG-Based Gesture Recognition. IEEE transactions on neural systems and rehabilitation engineering. ISSN 1534-4320. doi: 10.1109/TNSRE.2021.3059741. Vis sammendrag

  Within the field of electromyography-based (EMG) gesture recognition, disparities exist between the offline accuracy reported in the literature and the real-time usability of a classifier. This gap mainly stems from two factors: 1) The absence of a controller, making the data collected dissimilar to actual control. 2) The difficulty of including the four main dynamic factors (gesture intensity, limb position, electrode shift, and transient changes in the signal), as including their permutations drastically increases the amount of data to be recorded. Contrarily, online datasets are limited to the exact EMG-based controller used to record them, necessitating the recording of a new dataset for each control method or variant to be tested. Consequently, this paper proposes a new type of dataset to serve as an intermediate between offline and online datasets, by recording the data using a real-time experimental protocol. The protocol, performed in virtual reality, includes the four main dynamic factors and uses an EMG-independent controller to guide movements. This EMG-independent feedback ensures that the user is in-the-loop during recording, while enabling the resulting dynamic dataset to be used as an EMG-based benchmark. The dataset is comprised of 20 able-bodied participants completing three to four sessions over a period of 14 to 21 days. The ability of the dynamic dataset to serve as a benchmark is leveraged to evaluate the impact of different recalibration techniques for long-term (across-day) gesture recognition, including a novel algorithm, named TADANN. TADANN consistently and significantly ( p<0.05 ) outperforms using fine-tuning as the recalibration technique.

• Erdem, Cagri; Jensenius, Alexander Refsum; Glette, Kyrre; Krzyzaniak, Michael Joseph & Veenstra, Frank (2020). Air-Guitar Control of Interactive Rhythmic Robots. Proceedings of the International Conference on Live Interfaces (Proceedings of ICLI). ISSN 2663-9041. s. 208–210. Vis sammendrag

  This paper describes an interactive art installation shown at ICLI in Trondheim in March 2020. The installation comprised three musical robots (Dr. Squiggles) that play rhythms by tapping. Visitors were invited to wear muscle-sensor armbands, through which they could control the robots by performing ‘air-guitar’-like gestures.

• Nygaard, Tønnes; Howard, David & Glette, Kyrre (2020). Real world morphological evolution is feasible. I Coello Coello, Carlos A. (Red.), GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery (ACM). ISSN 978-1-4503-7127-8. s. 1392–1394. doi: 10.1145/3377929.3398095. Vis sammendrag

  Evolutionary algorithms offer great promise for the automatic design of robot bodies, tailoring them to specific environments or tasks. Most research is done on simplified models or virtual robots in physics simulators, which do not capture the natural noise and richness of the real world. Very few of these virtual robots are built as physical robots, and the few that are will rarely be further improved in the actual environment they operate in, limiting the effectiveness of the automatic design process. We utilize our shape-shifting quadruped robot, which allows us to optimize the design in its real-world environment. The robot is able to change the length of its legs during operation, and is robust enough for complex experiments and tasks. We have co-evolved control and morphology in several different scenarios, and have seen that the algorithm is able to exploit the dynamic morphology solely through real-world experiments.

• Engebråten, Sondre Andreas; Moen, Hans Jonas Fossum; Yakimenko, Oleg A. & Glette, Kyrre (2020). A framework for automatic behavior generation in multi-function swarms. Frontiers in Robotics and AI. ISSN 2296-9144. 7:579403, s. 1–19. doi: 10.3389/frobt.2020.579403. Fulltekst i vitenarkiv
• Milojevic, Andrija & Glette, Kyrre (2020). Design Optimization of Terrestrial-Walking Soft Robots, Proceedings of The Fifth International Conference – MASING 2020. Faculty of Mechanical Engineering in Niš. ISSN 978-86-6055-139-1. s. 225–228.
• Nordmoen, Jørgen Halvorsen; Veenstra, Frank; Ellefsen, Kai Olav & Glette, Kyrre (2020). Quality and Diversity in Evolutionary Modular Robotics, 2020 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-7281-2547-3. s. 2109–2116. doi: 10.1109/SSCI47803.2020.9308511.
• Barnes, Chloe M; Ekárt, Anikó; Ellefsen, Kai Olav; Glette, Kyrre; Lewis, Peter R. & Tørresen, Jim (2020). Coevolutionary Learning of Neuromodulated Controllers for Multi-Stage and Gamified Tasks, 2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-7281-7278-1. s. 129–138. doi: 10.1109/ACSOS49614.2020.00034.
• Côté-Allard, Ulysse; Gagnon-Turcotte, Gabriel; Phinyomark, Angkoon; Glette, Kyrre; Scheme, Erik & Laviolette, Francois (2020). Unsupervised Domain Adversarial Self-Calibration for Electromyography-Based Gesture Recognition. IEEE Access. ISSN 2169-3536. 8, s. 177941–177955. doi: 10.1109/ACCESS.2020.3027497. Fulltekst i vitenarkiv Vis sammendrag

  Surface electromyography (sEMG) provides an intuitive and non-invasive interface from which to control machines. However, preserving the myoelectric control system’s performance over multiple days is challenging, due to the transient nature of the signals obtained with this recording technique. In practice, if the system is to remain usable, a time-consuming and periodic recalibration is necessary. In the case where the sEMG interface is employed every few days, the user might need to do this recalibration before every use. Thus, severely limiting the practicality of such a control method. Consequently, this paper proposes tackling the especially challenging task of unsupervised adaptation of sEMG signals, when multiple days have elapsed between each recording, by introducing Self-Calibrating Asynchronous Domain Adversarial Neural Network (SCADANN). SCADANN is compared with two state-of-the-art self-calibrating algorithms developed specifically for deep learning within the context of EMG-based gesture recognition and three state-of-the-art domain adversarial algorithms. The comparison is made both on an offline and a dynamic dataset (20 participants per dataset), using two different deep network architectures with two different input modalities (temporal-spatial descriptors and spectrograms). Overall, SCADANN is shown to substantially and systematically improves classification performances over no recalibration and obtains the highest average accuracy for all tested cases across all methods.

• Martin, Charles Patrick; Glette, Kyrre; Nygaard, Tønnes & Tørresen, Jim (2020). Understanding Musical Predictions With an Embodied Interface for Musical Machine Learning. Frontiers in Artificial Intelligence. ISSN 2624-8212. 3(6). doi: 10.3389/frai.2020.00006. Vis sammendrag

  Machine-learning models of music often exist outside the worlds of musical performance practice and abstracted from the physical gestures of musicians. In this work, we consider how a recurrent neural network (RNN) model of simple music gestures may be integrated into a physical instrument so that predictions are sonically and physically entwined with the performer's actions. We introduce EMPI, an embodied musical prediction interface that simplifies musical interaction and prediction to just one dimension of continuous input and output. The predictive model is a mixture density RNN trained to estimate the performer's next physical input action and the time at which this will occur. Predictions are represented sonically through synthesized audio, and physically with a motorized output indicator. We use EMPI to investigate how performers understand and exploit different predictive models to make music through a controlled study of performances with different models and levels of physical feedback. We show that while performers often favor a model trained on human-sourced data, they find different musical affordances in models trained on synthetic, and even random, data. Physical representation of predictions seemed to affect the length of performances. This work contributes new understandings of how musicians use generative ML models in real-time performance backed up by experimental evidence. We argue that a constrained musical interface can expose the affordances of embodied predictive interactions.

• Veenstra, Frank & Glette, Kyrre (2020). How Different Encodings Affect Performance and Diversification when Evolving the Morphology and Control of 2D Virtual Creatures. I Bongard, Josh (Red.), The 2020 Conference on Artificial Life. MIT Press. ISSN 2693-1508. s. 592–601. doi: 10.1162/isal_a_00295. Fulltekst i vitenarkiv Vis sammendrag

  A challenge in evolutionary robotics is the in parallel adaptation of morphologies and controllers. Here, we considered encoding methods for morphogenesis of 2D virtual creatures that can be created from directed trees. Using an evolutionary algorithm, we optimized locomotion in these virtual creatures and compared a direct encoding, an L-System, and two types of encodings that produce neural networks—a Compositional Pattern Producing Network (CPPN) and a Cellular Encoding (CE). We evaluated these encodings based on performance and diversification, and we introduced an OpenAI gym environment as a computationally inexpensive benchmark for exploring morphological evolution. The direct encoding and L-System generated more fit solutions compared to the network strategies. Considering morphological diversity, the direct encoding finds solutions more locally in the morphological search space, the L-System made larger jumps across this search space, and both network approaches also make larger jumps though find fewer solutions in this space. With these results we show how encodings exhibit different characteristics as developmental approaches. Since the genotype-phenotype mapping plays a major role in evolutionary robotics, further modifications using more complex tasks and environments can lead to a better understanding of morphogenesis and thereby improve how morphologies and controllers of robots are evolved.

• St-Onge, David; Côté-Allard, Ulysse; Glette, Kyrre Harald; Gosselin, Benoit & Beltrame, Giovanni (2019). Engaging with Robotic Swarms: Commands from Expressive Motion. ACM Transactions on Human-Robot Interaction. ISSN 2573-9522. doi: 10.1145/3323213.
• Côté-Allard, Ulysse; Latyr Fall, Cheikh; Drouin, Alexandre; Campeau-Lecours, Alexandre; Gosselin, Clément & Glette, Kyrre Harald [Vis alle 7 forfattere av denne artikkelen] (2019). Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning. IEEE transactions on neural systems and rehabilitation engineering. ISSN 1534-4320. doi: 10.1109/TNSRE.2019.2896269.
• Ahmadi, Arash; Till, Katharina; Hafting, Yngve; Schuttpelz, Mark; Bjørås, Magnar & Glette, Kyrre [Vis alle 8 forfattere av denne artikkelen] (2019). Additive manufacturing of laminar flow cells for single-molecule experiments. Scientific Reports. ISSN 2045-2322. 9(1). doi: 10.1038/s41598-019-53151-z. Fulltekst i vitenarkiv
• Nygaard, Tønnes Frostad; Nordmoen, Jørgen Halvorsen; Ellefsen, Kai Olav; Martin, Charles Patrick; Tørresen, Jim & Glette, Kyrre (2019). Experiences from Real-World Evolution with DyRET: Dynamic Robot for Embodied Testing, Nordic Artificial Intelligence Research and Development: Third Symposium of the Norwegian AI Society, NAIS 2019. Springer. ISSN 978-3-030-35664-4. s. 58–68. doi: https:/doi.org/10.1007/978-3-030-35664-4_6.
• St-Onge, David; Côté-Allard, Ulysse; Glette, Kyrre Harald; Benoit, Gosselin & Beltrame, Giovanni (2019). Engaging with Robotic Swarms: Commands from Expressive Motion. ACM Transactions on Human-Robot Interaction. ISSN 2573-9522. 8(2). doi: 10.1145/3323213. Fulltekst i vitenarkiv Vis sammendrag

  In recent years, researchers have explored human body posture and motion to control robots in more natural ways. These interfaces require the ability to track the body movements of the user in three dimensions. Deploying motion capture systems for tracking tends to be costly and intrusive and requires a clear line of sight, making them ill adapted for applications that need fast deployment. In this article, we use consumer-grade armbands, capturing orientation information and muscle activity, to interact with a robotic system through a state machine controlled by a body motion classifier. To compensate for the low quality of the information of these sensors, and to allow a wider range of dynamic control, our approach relies on machine learning. We train our classifier directly on the user to recognize (within minutes) which physiological state his or her body motion expresses. We demonstrate that on top of guaranteeing faster field deployment, our algorithm performs better than all comparable algorithms, and we detail its configuration and the most significant features extracted. As the use of large groups of robots is growing, we postulate that their interaction with humans can be eased by our approach. We identified the key factors to stimulate engagement using our system on 27 participants, each creating his or her own set of expressive motions to control a swarm of desk robots. The resulting unique dataset is available online together with the classifier and the robot control scripts.

• Miseikis, Justinas; Brijacak, Inka; Yahyanejad, Saeed; Glette, Kyrre; Elle, Ole Jacob & Tørresen, Jim (2019). Two-Stage Transfer Learning for Heterogeneous Robot Detection and 3D Joint Position Estimation in a 2D Camera Image Using CNN. IEEE International Conference on Robotics and Automation (ICRA). ISSN 1050-4729. 2019-May, s. 8883–8889. doi: 10.1109/ICRA.2019.8794077. Fulltekst i vitenarkiv
• Nygaard, Tønnes Frostad; Martin, Charles Patrick; Tørresen, Jim & Glette, Kyrre (2019). Self-Modifying Morphology Experiments with DyRET: Dynamic Robot for Embodied Testing. IEEE International Conference on Robotics and Automation (ICRA). ISSN 1050-4729. 2019-May, s. 9446–9452. doi: 10.1109/ICRA.2019.8793663. Fulltekst i vitenarkiv
• Nordmoen, Jørgen Halvorsen; Nygaard, Tønnes Frostad; Ellefsen, Kai Olav & Glette, Kyrre (2019). Evolved embodied phase coordination enables robust quadruped robot locomotion. I López-Ibáñez, Manuel (Red.), GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference. Association for Computing Machinery (ACM). ISSN 978-1-4503-6111-8. s. 133–141. doi: 10.1145/3321707.3321762. Fulltekst i vitenarkiv
• Nygaard, Tønnes Frostad; Martin, Charles Patrick; Tørresen, Jim & Glette, Kyrre (2019). Evolving Robots on Easy Mode: Towards a Variable Complexity Controller for Quadrupeds. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 11454 LNCS, s. 616–632. doi: 10.1007/978-3-030-16692-2_41. Fulltekst i vitenarkiv
• Côté-Allard, Ulysse; Latyr Fall, Cheikh; Drouin, Alexandre; Campeau-Lecours, Alexandre; Gosselin, Clément & Glette, Kyrre [Vis alle 7 forfattere av denne artikkelen] (2019). Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning. IEEE transactions on neural systems and rehabilitation engineering. ISSN 1534-4320. 27(4), s. 760–771. doi: 10.1109/TNSRE.2019.2896269. Fulltekst i vitenarkiv Vis sammendrag

  In recent years, deep learning algorithms have become increasingly more prominent for their unparalleled ability to automatically learn discriminant features from large amounts of data. However, within the field of electromyography-based gesture recognition, deep learning algorithms are seldom employed as they require an unreasonable amount of effort from a single person, to generate tens of thousands of examples. This paper's hypothesis is that general, informative features can be learned from the large amounts of data generated by aggregating the signals of multiple users, thus reducing the recording burden while enhancing gesture recognition. Consequently, this paper proposes applying transfer learning on aggregated data from multiple users while leveraging the capacity of deep learning algorithms to learn discriminant features from large datasets. Two datasets comprised 19 and 17 able-bodied participants, respectively (the first one is employed for pre-training), were recorded for this work, using the Myo armband. A third Myo armband dataset was taken from the NinaPro database and is comprised ten able-bodied participants. Three different deep learning networks employing three different modalities as input (raw EMG, spectrograms, and continuous wavelet transform (CWT)) are tested on the second and third dataset. The proposed transfer learning scheme is shown to systematically and significantly enhance the performance for all three networks on the two datasets, achieving an offline accuracy of 98.31% for 7 gestures over 17 participants for the CWT-based ConvNet and 68.98% for 18 gestures over 10 participants for the raw EMG-based ConvNet. Finally, a use-case study employing eight able-bodied participants suggests that real-time feedback allows users to adapt their muscle activation strategy which reduces the degradation in accuracy normally experienced over time.

• Jelisavcic, Milan; Glette, Kyrre; Haasdijk, Evert & Eiben, AE (2019). Lamarckian Evolution of Simulated Modular Robots. Frontiers in Robotics and AI. ISSN 2296-9144. 6(9). doi: 10.3389/frobt.2019.00009. Fulltekst i vitenarkiv Vis sammendrag

  We study evolutionary robot systems where not only the robot brains but also the robot bodies are evolvable. Such systems need to include a learning period right after ‘birth' to acquire a controller that fits the newly created body. In this paper we investigate the possibility of bootstrapping infant robot learning through employing Lamarckian inheritance of parental controllers. In our system controllers are encoded by a combination of a morphology dependent component, a Central Pattern Generator (CPG), and a morphology independent part, a Compositional Pattern Producing Network (CPPN). This makes it possible to transfer the CPPN part of controllers between different morphologies and to create a Lamarckian system. We conduct experiments with simulated modular robots whose fitness is determined by the speed of locomotion, establish the benefits of inheriting optimized parental controllers, shed light on the conditions that influence these benefits, and observe that changing the way controllers are evolved also impacts the evolved morphologies.

• Engebråten, Sondre Andreas; Yakimenko, Oleg; Moen, Hans Jonas Fossum & Glette, Kyrre (2018). Towards a Multi-Function Swarm That Adapts to User Preferences, Swarms: From Biology to Robotics and Back. ICRA 2018 Workshop. Harvard University. Fulltekst i vitenarkiv
• Miseikis, Justinas; Knobelreiter, Patrick; Brijacak, Inka; Yahyanejad, Saeed; Glette, Kyrre & Elle, Ole Jacob (2018). Robot localisation and 3D position estimation using a free-moving camera and cascaded convolutional neural networks, 2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2018). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781538618554. s. 181–187. doi: 10.1109/AIM.2018.8452236. Fulltekst i vitenarkiv
• Miseikis, Justinas; Brijacak, Inka; Yahyanejad, Saeed; Glette, Kyrre; Elle, Ole Jacob & Tørresen, Jim (2018). Multi-Objective Convolutional Neural Networks for Robot Localisation and 3D Position Estimation in 2D Camera Images, 2018 15th International Conference on Ubiquitous Robots (UR 2018). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781538663356. s. 597–603. doi: 10.1109/URAI.2018.8441813. Fulltekst i vitenarkiv
• Miseikis, Justinas; Brijacak, Inka; Yahyanejad, Saeed; Glette, Kyrre; Elle, Ole Jacob & Tørresen, Jim (2018). Transfer Learning for Unseen Robot Detection and Joint Estimation on a Multi-Objective Convolutional Neural Network, 2018 IEEE International Conference on Intelligence and Safety for Robotics (ISR 2018). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781538655481. s. 337–342. doi: 10.1109/IISR.2018.8535937. Fulltekst i vitenarkiv
• Miras, Karine; Gansekoele, Arwin; Glette, Kyrre & Eiben, AE (2018). Insights in evolutionary exploration of robot morphology spaces. I Sundaram, Suresh (Red.), Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence (SSCI 2018). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-5386-9275-2. s. 867–874. doi: 10.1109/SSCI.2018.8628662. Fulltekst i vitenarkiv
• Miras, Karine; Haasdijk, Evert; Glette, Kyrre & Eiben, AE (2018). Effects of Selection Preferences on Evolved Robot Morphologies and Behaviors, The 2018 Conference on Artificial Life. MIT Press. ISSN 9780262355766. s. 224–231. doi: 10.1162/isal_a_00047. Fulltekst i vitenarkiv Vis sammendrag

  This paper investigates the evolution of modular robots using different selection preferences (i.e., fitness functions), aiming at novelty, speed of locomotion, number of limbs, and com- binations of these. The outcomes are analyzed from differ- ent perspectives: sampling of the search space, evolved mor- phologies, and evolved behaviors. This results in a wealth of findings, including a surprise about the number of sampled regions of the search space and the effect of different fitness functions on the evolved morphologies.

• Ahmadi, Arash; Rosnes, Ida; Blicher, Pernille; Diekmann, Robin; Schüttpelz, Mark & Glette, Kyrre [Vis alle 9 forfattere av denne artikkelen] (2018). Breaking the speed limit with multimode fast scanning of DNA by Endonuclease V. Nature Communications. ISSN 2041-1723. 9(1). doi: 10.1038/s41467-018-07797-4. Fulltekst i vitenarkiv
• Engebråten, Sondre Andreas; Glette, Kyrre & Yakimenko, Oleg (2018). Networking-enabling enhancement for a swarm of COTS Drones. IEEE International Conference on Control and Automation. ISSN 1948-3449. 2018-June, s. 562–569. doi: 10.1109/ICCA.2018.8444274.
• Engebråten, Sondre Andreas; Glette, Kyrre & Yakimenko, Oleg (2018). Field-Testing of High-Level Decentralized Controllers for a Multi-Function Drone Swarm. IEEE International Conference on Control and Automation. ISSN 1948-3449. 2018-June, s. 379–386. doi: 10.1109/ICCA.2018.8444354.

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• Søyseth, Vegard; Bergflødt, Adrian Anderson; Glette, Kyrre; Watanabe, Shin & van Otterdijk, Marieke (2024). Utstilling av roboter på SENT @ Teknisk museum.
• Veenstra, Frank; Norstein, Emma Stensby & Glette, Kyrre (2023). Tutorial: Evolving Robot Bodies and Brains with Unity. Vis sammendrag

  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.

• (2023). Evolutionary and adaptive robotics: from simulation to reality .
• (2023). Bio-inspiration for robot design and adaptation .
• (2023). Adaptive robots through evolutionary algorithms and machine learning.
• Fuhrer, Julian; Glette, Kyrre; Ivanovic, Jugoslav; Larsson, Pål Gunnar; Bekinschtein, Tristan & Kochen, Silvia [Vis alle 10 forfattere av denne artikkelen] (2022). Direct brain recordings reveal continuous encoding of structure in random stimuli.
• Fuhrer, Julian; Blenkmann, Alejandro Omar; Endestad, Tor; Solbakk, Anne-Kristin & Glette, Kyrre (2022). Complexity-Based Encoded Information Quantification in Neurophysiological Recordings.
• Szorkovszky, Alexander; Veenstra, Frank & Glette, Kyrre (2022). From real-time adaptation to social learning in robots.
• Fuhrer, Julian; Glette, Kyrre; Ivanovic, Jugoslav; Larsson, Pål Gunnar; Bekinschtein, Tristan & Kochen, Silvia [Vis alle 10 forfattere av denne artikkelen] (2022). Direct brain recordings reveal continuous encoding of structure in random stimuli.
• Howard, David; Glette, Kyrre & Cheney, Nick (2022). Editorial: Evolving Robotic Morphologies. Frontiers in Robotics and AI. ISSN 2296-9144. 9. doi: 10.3389/frobt.2022.874853.
• Krzyzaniak, Michael Joseph; Gerry, Jennifer; Kwak, Dongho; Erdem, Cagri; Lan, Qichao & Glette, Kyrre (2021). Fibres Out of Line. Vis sammendrag

  Fibres Out of Line is an interactive art installation and performance for the 2021 Rhythm Perception and Production Workshop (RPPW). Visitors can watch the performance, and subsequently interact with the installation, all remotely via Zoom.

• Jensenius, Alexander Refsum; Erdem, Cagri; Kwak, Dongho Daniel; Rahman, Habibur; Glette, Kyrre & Krzyzaniak, Michael Joseph [Vis alle 7 forfattere av denne artikkelen] (2020). Strings On-Line. Vis sammendrag

  An interactive art installation presented during the International Conference on New Interfaces for Musical Expression (NIME) 2020.

• Krzyzaniak, Michael Joseph; Veenstra, Frank; Erdem, Cagri; Glette, Kyrre & Jensenius, Alexander Refsum (2020). Interactive Rhythmic Robots.
• Krzyzaniak, Michael Joseph; Kwak, Dongho Daniel; Veenstra, Frank; Erdem, Cagri; Wallace, Benedikte & Jensenius, Alexander Refsum (2020). Dr. Squiggles rhythmical robots. Vis sammendrag

  Dr. Squiggles is an interactive musical robot that we designed, that plays rhythms by tapping. It listens for tapping produced by humans or other musical robots, and attempts to play along and improvise its own rhythms based on what it hears.

• (2020). Evolutionary algorithms for intelligent robots.
• Fuhrer, Julian; Blenkmann, Alejandro Omar; Tørresen, Jim; Endestad, Tor & Glette, Kyrre (2019). Making Sense of Randomness?
• Glette, Kyrre; Nygaard, Tønnes Frostad & Vogt, Yngve (2019). Her er universitetets nest selvlærende robot. [Fagblad]. Teknisk ukeblad.
• (2019). Her er universitetets mest avanserte, selvlærende robot. [Fagblad]. Apollon.
• Tørresen, Jim; Glette, Kyrre & Ellefsen, Kai Olav (2019). Intelligent, Adaptive Robots in Real-World Scenarios.
• Tørresen, Jim; Glette, Kyrre & Ellefsen, Kai Olav (2019). Adaptive Robot Body and Control for Real-World Environments.
• Miseikis, Justinas; Brijacak, Inka; Yahyanejad, Saeed; Glette, Kyrre; Elle, Ole Jacob & Tørresen, Jim (2019). Two-Stage Transfer Learning for Heterogeneous Robot Detection and 3D Joint Position Estimation in a 2D Camera Image Using CNN.
• (2019). Kunstig intelligens for tilpasningsdyktige roboter .
• Nygaard, Tønnes Frostad; Nordmoen, Jørgen Halvorsen; Martin, Charles Patrick; Tørresen, Jim & Glette, Kyrre (2019). Lessons Learned from Real-World Experiments with DyRET: the Dynamic Robot for Embodied Testing.
• Nygaard, Tønnes Frostad; Martin, Charles Patrick; Tørresen, Jim & Glette, Kyrre (2019). Self-Modifying Morphology Experiments with DyRET: Dynamic Robot for Embodied Testing.
• Nygaard, Tønnes Frostad; Nordmoen, Jørgen Halvorsen; Ellefsen, Kai Olav; Martin, Charles Patrick; Tørresen, Jim & Glette, Kyrre (2019). Experiences from Real-World Evolution with DyRET: Dynamic Robot for Embodied Testing.
• Nordmoen, Jørgen Halvorsen; Nygaard, Tønnes Frostad; Ellefsen, Kai Olav & Glette, Kyrre (2019). Evolved embodied phase coordination enables robust quadruped robot locomotion .
• Ahmadi, Arash; Rosnes, Ida; Blicher, Pernille; Diekmann, Robin; Schuttpelz, Mark & Glette, Kyrre [Vis alle 9 forfattere av denne artikkelen] (2019). Publisher Correction: Breaking the speed limit with multimode fast scanning of DNA by Endonuclease V (Nature Communications, (2018), 9, 1, (5381), 10.1038/s41467-018-07797-4). Nature Communications. ISSN 2041-1723. 10(1). doi: 10.1038/s41467-019-10070-x.
• Jensenius, Alexander Refsum; Martin, Charles Patrick; Erdem, Cagri; Lan, Qichao; Fuhrer, Julian Peter & Gonzalez Sanchez, Victor Evaristo [Vis alle 8 forfattere av denne artikkelen] (2019). Self-playing Guitars. Vis sammendrag

  In this installation we explore how six self-playing guitars can entrain to each other. When they are left alone they will revert to playing a common pulse. As soon as they sense people in their surroundings they will start entraining to other pulses. The result is a fascinating exploration of a basic physical and cognitive concept, and the musically interesting patterns that emerge on the border between order and chaos.

• (2019). Kunstig intelligens for tilpasningsdyktige roboter.
• Fuhrer, Julian Peter; Glette, Kyrre & Jensenius, Alexander Refsum (2018). Interactive Animation of the RITMO Logo. Vis sammendrag

  In this project the logo of RITMO is installed in an interactive animation. It is able to move in accordance with the frequency band of an audio input stream. That is to say, the RITMO logo interacts with the rhythmical streams of music.

• Fuhrer, Julian; Glette, Kyrre & Jensenius, Alexander Refsum (2018). Interactive Animation of RITMO Logo.
• Fuhrer, Julian; Glette, Kyrre & Jensenius, Alexander Refsum (2018). Interactive Opening Animation. Vis sammendrag

  With this project we installed the logo of RITMO in an interactive animation for the opening of the centre. The logo is enabled to receive audio input such that it is able to move in accordance with the frequency band of the input. That is to say, the logo is able to move along with rythmic streams of the music.

• (2018). Robotics, Intelligent Systems and Evolutionary Computation .
• (2018). Automatic design of shapes and behaviors for robots.
• Engebråten, Sondre Andreas; Yakimenko, Oleg; Moen, Hans Jonas Fossum & Glette, Kyrre (2018). Evolving a Repertoire of Controllers for a Multi-Function Swarm.
• Engebråten, Sondre Andreas; Yakimenko, Oleg; Moen, Hans Jonas Fossum & Glette, Kyrre (2018). Towards a Multi-Function Swarm That Adapts to User Preferences .
• Nordmoen, Jørgen; Glette, Kyrre & Ellefsen, Kai Olav (2021). Enhancing MAP-Elites to overcome challenges in Evolutionary Robotics. University of Oslo. ISSN 1501-7710.
• Nygaard, Tønnes; Glette, Kyrre; Tørresen, Jim & Martin, Charles Patrick (2020). Legging It: An Evolutionary Approach to Morphological Adaptation for a Real-World Quadruped Robot. Universitetet i Oslo. ISSN 1501-7710. Fulltekst i vitenarkiv
• Lee, Wonho & Glette, Kyrre (2020). A Neuroevolutionary Approach to Evolve a Flexible Neural Controller for a Morphology Changing Quadruped Robot. Universitetet i Oslo.
• Stensby, Emma Hjellbrekke & Glette, Kyrre (2020). Co-evolving morphology and controller in an open-ended environment. Universitetet i Oslo.

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