Tønnes Nygaard

• 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
• Nilsen, Mathias Kvamme; Nygaard, Tønnes & Ellefsen, Kai Olav (2023). Reward tampering and evolutionary computation: a study of concrete AI-safety problems using evolutionary algorithms. Genetic Programming and Evolvable Machines. ISSN 1389-2576. 24(2). doi: 10.1007/s10710-023-09456-0. 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
• (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
• 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
• Ahmadi, Ahmadreza; Nygaard, Tønnes; Kottege, Navinda; Howard, David & Hudson, Nicolas (2021). Semi-Supervised Gated Recurrent Neural Networks for Robotic Terrain Classification. IEEE Robotics and Automation Letters. ISSN 2377-3766. 6(2), s. 1848–1855. doi: 10.1109/LRA.2021.3060437.
• 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.

• Forberg, Henrik Storm; Nygaard, Tønnes & Høvin, Mats Erling (2020). Generative Truss Optimization for Support-Free Fused Filament Fabrication. I Arabnia, Hamid R.; Deligiannidis, Leonidas; Tinetti, Fernando G. & Tran, Quoc-Nam (Red.), Proceedings of 2020 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE Computer Society Digital Library. ISSN 9781728176246. s. 149–1497.
• 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.

• 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.

• 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.
• 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
• Nygaard, Tønnes Frostad; Martin, Charles Patrick; Samuelsen, Eivind; Tørresen, Jim & Glette, Kyrre (2018). Real-world evolution adapts robot morphology and control to hardware limitations. I aguirre, hernan (Red.), GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference. Association for Computing Machinery (ACM). ISSN 978-1-4503-5618-3. s. 125–132. doi: 10.1145/3205455.3205567. Fulltekst i vitenarkiv Vis sammendrag

  For robots to handle the numerous factors that can affect them in the real world, they must adapt to changes and unexpected events. Evolutionary robotics tries to solve some of these issues by automatically optimizing a robot for a specific environment. Most of the research in this field, however, uses simplified representations of the robotic system in software simulations. The large gap between performance in simulation and the real world makes it challenging to transfer the resulting robots to the real world. In this paper, we apply real world multi-objective evolutionary optimization to optimize both control and morphology of a four-legged mammal-inspired robot. We change the supply voltage of the system, reducing the available torque and speed of all joints, and study how this affects both the fitness, as well as the morphology and control of the solutions. In addition to demonstrating that this real-world evolutionary scheme for morphology and control is indeed feasible with relatively few evaluations, we show that evolution under the different hardware limitations results in comparable performance for low and moderate speeds, and that the search achieves this by adapting both the control and the morphology of the robot.

• Nygaard, Tønnes Frostad; Samuelsen, Eivind & Glette, Kyrre (2017). Overcoming initial convergence in multi-objective evolution of robot control and morphology using a two-phase approach. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 10199 LNCS(Part I), s. 825–836. doi: 10.1007/978-3-319-55849-3_53. Fulltekst i vitenarkiv
• Nygaard, Tønnes Frostad; Tørresen, Jim & Glette, Kyrre (2016). Multi-objective Evolution of Fast and Stable Gaits on a Physical Quadruped Robotic Platform. I Jin, Yaochu & Kollias, Stefanos (Red.), Proc. of 2016 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE conference proceedings. ISSN 978-1-5090-4240-1. doi: 10.1109/SSCI.2016.7850167.

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• (2023). Selvgående drapsmaskiner - Forsvarets nye leketøy vekker oppsikt. [Fagblad]. Vi Menn.
• (2023). Roboten Freke – Hvordan kan kunstig intelligens bidra til å øke Norges sikkerhet og beredskap?
• Nygaard, Tønnes Frostad & Bushwick, Sophie (2023). Mighty Morpher. [Fagblad]. Scientific American.
• (2022). Robothunden – menneskets nye bestevenn?
• (2022). Norsk, sivil teknologi fornyer forsvaret: - Vi står overfor en revolusjon. [Internett]. kode24.no.
• (2022). Robothunder og selvkjørende biler tar Forsvaret inn i fremtiden . [Internett]. Forsvaret.no.
• Nygaard, Tønnes Frostad & Simon, Matt (2022). This Cheetah Robot Taught Itself How to Sprint in a Weird Way. [Internett]. Wired.com.
• (2022). Roboten Freke på åpningen av museumsutstillingen I/O på Teknisk Museum.
• Elverhøy, Hilmar; Bøe, Steinar; Søyseth, Vegard & Nygaard, Tønnes (2021). Ankle Joints Are Beneficial When Optimizing Supported Real-world Bipedal Robot Gaits. arXiv. doi: 10.48550/arXiv.2105.10764.
• (2021). Morphology Adaptation in Real-world Robots.
• Nygaard, Tønnes Frostad & Spry, Jeff (2021). Meet Spacebok, the radical new rover built to jump around the moon and mars. [Internett]. syfy.com.
• Nygaard, Tønnes Frostad & Simon, Matt (2021). See little robots get swole in this virtual ‘gym’. [Internett]. Wired.com.
• Nygaard, Tønnes Frostad & Simon, Matt (2021). How Do You Make a Robot Walk on Mars? It’s a Steep Challenge. [Internett]. Wired.com.
• Nygaard, Tønnes Frostad & Simon, Matt (2021). Watch a Shape-Shifting Robot Prowl the Big, Bad World. [Internett]. Wired.com.
• Nygaard, Tønnes Frostad & Torgersen, Eivind (2021). Denne roboten endrer lengden på beina når den går fra gress til betong. [Internett]. Forskning.no.
• Nygaard, Tønnes Frostad; Leirvik, Jan børge; Grov, Benedikte & Marvik, Jo Arne (2021). Robothund kan bli vaktmeisterens beste venn. [Internett]. NRK.
• Nygaard, Tønnes Frostad & Simon, Matt (2020). Meet Xenobot, an Eerie New Kind of Programmable Organism. [Internett]. Wired.com.
• Ellefsen, Kai Olav; Nygaard, Tønnes Frostad & Kjørstad, Elise (2020). Her er den første roboten som er laget av levende celler. [Internett]. forskning.no.
• 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.
• 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 & Simon, Matt (2019). Inside Facebook's New Robotics Lab, Where AI and Machines Friend One Another. [Fagblad]. Wired.
• 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 .
• Nygaard, Tønnes Frostad & Simon, Matt (2019). The Clever Clumsiness of a Robot Teaching Itself to Walk. [Fagblad]. Wired Science.
• (2018). DyRET: Dynamic Robot for Embodied Testing.
• Søyseth, Vegard Dønnem; Nygaard, Tønnes Frostad; Martin, Charles Patrick; Uddin, Md Zia & Ellefsen, Kai Olav (2018). ROBIN-Stand ved Cutting Edge 2018.
• Martin, Charles Patrick; Glette, Kyrre; Nygaard, Tønnes Frostad & Tørresen, Jim (2018). Self-Awareness in a Cyber-Physical Predictive Musical Interface. Vis sammendrag

  We introduce a new self-contained and self-aware interface for musical expression where a recurrent neural network (RNN) is integrated into a physical instrument design. The system includes levers for physical input and output, a speaker system, and an integrated single-board computer. The RNN serves as an internal model of the user’s physical input, and predictions can replace or complement direct sonic and physical control by the user. We explore this device in terms of different interaction configurations and learned models according to frameworks of self-aware cyber-physical systems.

• Moen, Hans Jonas Fossum; Glette, Kyrre; Nygaard, Tønnes Frostad & Johnsrud, Mette (2018). Fem felt der vi får en førerløs fremtid. [Internett]. Titan.uio.no.
• Nygaard, Tønnes Frostad; Martin, Charles Patrick; Tørresen, Jim & Glette, Kyrre (2018). Exploring Mechanically Self-Reconfiguring Robots for Autonomous Design. Vis sammendrag

  Evolutionary robotics has aimed to optimize robot control and morphology to produce better and more robust robots. Most previous research only addresses optimization of control, and does this only in simulation. We have developed a four-legged mammal-inspired robot that features a self-reconfiguring morphology. In this paper, we discuss the possibilities opened up by being able to efficiently do experiments on a changing morphology in the real world. We discuss present challenges for such a platform and potential experimental designs that could unlock new discoveries. Finally, we place our robot in its context within general developments in the field of evolutionary robotics, and consider what advances the future might hold.

• Nygaard, Tønnes Frostad; Søyseth, Vegard Dønnem; Nordmoen, Jørgen Halvorsen & Glette, Kyrre (2018). Stand with the DyRET robot.
• (2018). Real-World Evolution Adapts Robot Morphology and Control to Hardware Limitations.
• Nygaard, Tønnes Frostad & Nordmoen, Jørgen Halvorsen (2018). Dynamic Robot for Embodied Testing, Open Source Material.
• Nygaard, Tønnes Frostad & Gonzales, Robbie (2018). How a Flock of Drones Developed Collective Intelligence. [Fagblad]. Wired Science.
• Nygaard, Tønnes Frostad & Dormehl, Luke (2018). This robot taught itself how to walk and it’s as clumsy as a newborn deer. [Fagblad]. Digital Trends.
• Nygaard, Tønnes Frostad & Papadopoulos, Loukia (2018). New Evolving Robot Teaches Itself to Walk Through Trial and Error. [Fagblad]. Interesting Engineering.
• Nygaard, Tønnes Frostad & Khattar, Shivani (2018). How a shape-shifting robot is learning from its mistakes. [TV]. NBC news.
• Nygaard, Tønnes Frostad & Simon, Matt (2018). How Roboticists Are Copying Nature To Make Fantastical Machines. [Internett]. Wired Science.
• Nygaard, Tønnes Frostad & Simon, Matt (2018). The shape-shifting robot that evolves by falling down. [Fagblad]. Wired Science.
• Nygaard, Tønnes Frostad & Eikenes, Åsmund H. (2017). Robotane må tilpasse seg oss menneske. [Fagblad]. Framtida.no.
• Martin, Charles Patrick; Gonzalez Sanchez, Victor Evaristo; Kelkar, Tejaswinee; Zelechowska, Agata; Berggren, Stig Johan & Hopgood, Christina [Vis alle 13 forfattere av denne artikkelen] (2017). Ensemble Metatone - Improvised Touchscreen Performance.
• (2017). Presentation on Robotics: Today and the future.
• Nygaard, Tønnes Frostad & Haugnes, Gunhild (2016). Nå kommer robotene som lærer som et menneske. [Fagblad]. Titan.
• Glette, Kyrre Harald; Nygaard, Tønnes Frostad; Grutle, Øyvind Kallevik; Miseikis, Justinas & Drews, Haakon (2015). Robotikk og intelligente systemer (stand).
• Glette, Kyrre Harald; Høvin, Mats Erling; Samuelsen, Eivind; Nygaard, Tønnes Frostad & Temperton, James (2014). 3D printed evolving robots destined for disaster zones. wired.co.uk.
• Vogt, Yngve; Samuelsen, Eivind; Glette, Kyrre Harald; Nygaard, Tønnes Frostad & Høvin, Mats Erling (2014). Skriver ut selvlærende roboter. Apollon : Forskningsmagasin for Universitetet i Oslo. ISSN 0803-6926.
• Glette, Kyrre Harald; Samuelsen, Eivind & Nygaard, Tønnes Frostad (2014). Evolusjonær robotikk.
• 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
• Chou, Jiader; Glette, Kyrre & Nygaard, Tønnes Frostad (2017). Terrain classification using 3D optical tactile sensor. Universitetet i Oslo.
• Fahle, Hans Fredrik; Fahle, Hans Fredrik; Glette, Kyrre & Nygaard, Tønnes Frostad (2017). Predicting Gaits Based on Prior Experience. Universitetet i Oslo.
• Thorstensen, Mathias Ciarlo; Glette, Kyrre & Nygaard, Tønnes Frostad (2017). Visualization of Robotic Sensor Data with Augmented Reality. Universitetet i Oslo.

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