Publications

2022

Baum, M., & Brock, O. (2022). “The World Is Its Own Best Model”: Robust Real-World Manipulation Through Online Behavior Selection. ICRA 2022.
Bolenz, F., & Eppinger, B. (2022). Valence bias in metacontrol of decision making in adolescents and young adults. Child Development. https://doi.org/10.1111/cdev.13693
Bolenz, F., & Pachur, T. (2022). Exploring the structure of predecisional information search in risky choice. Proceedings of the 44th Annual Conference of the Cognitive Science Society, 2297–2302.
Bolenz, F., Profitt, M., Stechbarth, F., Eppinger, B., & Strobel, A. (2022). Need for cognition does not account for individual differences in metacontrol of decision making. Scientific Reports. https://doi.org/https://doi.org/10.1038/s41598-022-12341-y
Chen, Y., Mikkelsen, J., Binder, A., Alt, C., & Hennig, L. (2022). A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition. Proceedings of the 7th Workshop on Representation Learning for NLP.
Chevalére, J., Kirtay, M., Hafner, V., & Lazarides, R. (2022). Who to Observe and Imitate in Humans and Robots: The Importance of Motivational Factors. International Journal of Social Robotics. https://doi.org/10.1007/s12369-022-00923-9
Coelho Mollo, D. (2022). Intelligent Behaviour. Erkenntnis. https://doi.org/https://doi.org/10.1007/s10670-022-00552-8
Doran, C., Bierbach, D., Lukas, J., Klamser, P., Landgraf, T., Klenz, H., Habedank, M., Arias-Rodriguez, L., Krause, S., Romanczuk, P., & Krause, J. (2022). Fish waves as emergent collective antipredator behavior. Current Biology. https://doi.org/https://doi.org/10.1016/j.cub.2021.11.068
Driess, D., Huang, Z., Li, Y., Tedrake, R., & Toussaint, M. (2022). Learning Multi-Object Dynamics with Compositional Neural Radiance Fields. CoRL 2022.
Driess, D., Schubert, I., Florence, P., Li, Y., & Toussaint, and M. (2022). Reinforcement Learning with Neural Radiance Fields. NeurIPS 2022.
Ghosh, S., & Gallego, G. (2022). Event-based Stereo Depth Estimation from Ego-motion using Ray Density Fusion. ECCVW Ego4D 2022.
Ha, J.-S., Driess, D., & Toussaint, M. (2022). Deep Visual Constraints: Neural Implicit Models for Manipulation Planning from Visual Input. IEEE Robotics and Automation Letters.
Halawa, M., Hellwich, O., & Bideau, P. (2022). Action based Contrastive Learning for Trajectory Prediction. ECCV. https://doi.org/https://arxiv.org/abs/2207.08664
Hamann, F., & Gallego, G. (2022). Stereo Co-capture System for Recording and Tracking Fish with Frame- and Event Cameras. International Conference on Pattern Recognition (ICPR), Workshop on Visual observation and analysis of Vertebrate And Insect Behavior.
Harbecke, D., Chen, Y., Hennig, L., & Alt, C. (2022). Why only Micro-F1? Class Weighting of Measures for Relation Classification. Proceedings of the 1st Workshop on Efficient Benchmarking in NLP.
Harris, J., Driess, D., & Toussaint, M. (2022). FC3: Feasibility-Based Control Chain Coordination. IROS 2022.
Henke, L., Guseva, M., Wagemans, K., Pischedda, D., Haynes, J.-D., Jahn, G., & Anders, S. (2022). Surgical face masks do not impair the decoding of facial expressions of negative affect more severely in older than in younger adults. Cognitive Research: Principles and Implications, 7, 63. https://doi.org/10.1186/s41235-022-00403-8
Kamat, J., Ortiz-Haro, J., Toussaint, M., Pokorny, F. T., & Orthey, A. (2022). BITKOMO: Combining Sampling and Optimization for Fast Convergence in Optimal Motion Planning. IROS 2022.
Lange, R., & Sprekeler, H. (2022). Learning not to learn: Nature versus Nurture in Silico. AAAI 2022. https://doi.org/10.48550/arXiv.2010.04466
Li, X., & Brock, O. (2022). Learning from Demonstration Based On Environmental Constraints. IEEE Robotics and Automation Letters with IROS Option.
Maier, M., Blume, F., Bideau, P., Hellwich, O., & Abdel Rahman, R. (2022). Knowledge-Augmented Face Perception: Prospects for the Bayesian Brain-Framework to Align AI and Human Vision. Consciousness and Cognition, 101. https://doi.org/
Maier, M., Leonhardt, A., & Rahman, R. A. (2022). Bad robots? Humans rapidly attribute mental states during the perception of robot faces. KogWis 2022.
Maier, M., Frömer, R., Rost, J., Sommer, W., & Rahman, R. A. (2022). Linguistic and semantic influences on early vision: evidence from object perception and mental imagery. Cognitive Neuroscience of Language Embodiment and Relativity.
Makowicz, A. M., Bierbach, D., Richardson, C., & Hughes, K. A. (2022). Cascading indirect genetic effects in a clonal vertebrate. ProcB, 2021.02.27.433187. https://doi.org/10.1101/2021.02.27.433187
Ortiz-Haro, J., Ha, J.-S., Driess, D., & Toussaint, M. (2022). Structured deep generative models for sampling on constraint manifolds in sequential manipulation. CoRL 2021.
Puhlmann, S., Harris, J., & Brock, O. (2022). RBO Hand 3 – A Platform for Soft Dexterous Manipulation. IEEE Transactions on Robotics. https://doi.org/https://doi.org/10.48550/arXiv.2201.10883
Reverberi, C., Pischedda, D., Mantovani, M., Haynes, J.-D., & Rustichini, A. (2022). Strategic complexity and cognitive skills affect brain response in interactive decision-making. Scientific Reports, 12, 15896. https://doi.org/10.1038/s41598-022-17951-0
Ruel, A., Bolenz, F., Li, S.-C., Fischer, A., & Eppinger, B. (2022). Neural evidence for age-related deficits in the representation of state spaces. Cerebral Cortex. https://doi.org/https://doi.org/10.1093/cercor/bhac171
Shiba, S., Aoki, Y., & Gallego, G. (2022). Secrets of Event-Based Optical Flow. European Conference on Computer Vision (ECCV), TODO, 23. https://doi.org/TODO
Toussaint, M., Harris, J., Ha, J.-S., Driess, D., & Hönig, W. (2022). Sequence-of-Constraints MPC: Reactive Timing-Optimal Control of Sequential Manipulation. arxiv.:2203.05390.
Toussaint, M., Harris, J., Ha, J.-S., Driess, D., & Hönig, W. (2022). Sequence-of-Constraints MPC: Reactive Timing-Optimal Control of Sequential Manipulation. IROS 2022.
Tump, A. N., Wolf, M., Romanczuk, P., & Kurvers, R. (2022). Avoiding costly mistakes in groups: The evolution of error management in collective decision making. PsyArXiv. https://doi.org/10.31234/osf.io/r4kd7
Tump, A., Pleskac, T., Romanczuk, P., & Kurvers, R. (2022). How the cognitive mechanisms underlying fast choices influence information spread and response bias amplification in groups. Proceedings of the 44th Annual Conference of the Cognitive Science Society, 44, 658–664.
Tump, A. N., Wolf, M., Romanczuk, P., & Kurvers, R. (2022). Avoiding costly mistakes in groups: The evolution of error management in collective decision making. PLOS Computational Biology. https://doi.org/https://doi.org/10.1371/journal.pcbi.1010442
Vischer, M., Lange, R., & Sprekeler, H. (2022). On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learning. ICLR 2022. https://doi.org/10.48550/arXiv.2105.01648
Yun, H. S., Taliaronak, V., Kirtay, M., Chevelère, J., Hübert, H., Hafner, V. V., Pinkwart, N., & Lazarides, R. (2022). Challenges in Designing Teacher Robots with Motivation Based Gestures. 17th Annual ACM/IEEE International Conference on Human-Robot Interaction (HRI 2022).
Yun, H. S., Hübert, H., Taliaronak, V., Mayet, R., Kirtay, M., Hafner, V. V., & Pinkwart, N. (2022). AI-based Open-Source Gesture Retargeting to a Humanoid Teaching Robot. AIED 2022: The 23rd International Conference on Artificial Intelligence in Education.
Yun, H. S., Hübert, H., Taliarona, V., & Sardogan, A. (2022). Utilizing Machine Learning based Gesture Recognition Software, Mediapipe, in the Context of Education and Health. AI Innovation Summit 2022.
Zhao, Y., Huepe, C., & Romanczuk, P. (2022). Contagion dynamics in self-organized systems of self-propelled particles. Scientific Reports. https://doi.org/https://doi.org/10.1038/s41598-022-06083-0

2021

Bak-Coleman, J. B., Alfano, M., Barfuss, W., Bergstrom, C. T., Centeno, M. A., Couzin, I. D., Donges, J. F., Galesic, M., Gersick, A. S., Jacquet, J., Kao, A. B., Moran, R. E., Romanczuk, P., Rubenstein, D. I., Tombak, K. J., Bavel, J. J. V., & Weber, E. U. (2021). Stewardship of global collective behavior. Proceedings of the National Academy of Sciences, 118(27). https://doi.org/10.1073/pnas.2025764118
Battaje, A., & Brock, O. (2021). Interconnected Recursive Filters in Artificial and Biological Vision. DGR Days 2021, 32–32.
Bhatt, A., Sieler, A., Puhlmann, S., & Brock, O. (2021). Surprisingly Robust In-Hand Manipulation: An Empirical Study. Proceedings of Robotics: Science and Systems. https://doi.org/10.15607/RSS.2021.XVII.089
Bierbach, D., Wenchel, R., Gehrig, S., Wersing, S., O'Connor, O. L., & Krause, J. (2021). Male Sexual Preference for Female Swimming Activity in the Guppy (Poecilia reticulata). Biology, 10(2). https://doi.org/10.3390/biology10020147
Bierbach, D., Francisco, F., Lukas, J., Landgraf, T., Maxeiner, M., Romanczuk, P., Musiolek, L., Hafner, V. V., & Krause, J. (2021). Biomimetic robots promote the 3Rs Principle in animal testing. ALIFE 2021: The 2021 Conference on Artificial Life. https://doi.org/10.1162/isal_a_00375
Chouzouris, T., Roth, N., Cakan, C., & Obermayer, K. (2021). Applications of optimal nonlinear control to a whole-brain network of FitzHugh-Nagumo oscillators. Physical Review E, 104(2), 24213. https://doi.org/10.1103/PhysRevE.104.024213
Coelho Mollo, D. (2021). Why go for a computation-based approach to cognitive representation. Synthese, 199(3), 6875–6895.
Coelho Mollo, D. (2021). Deflationary realism: Representation and idealisation in cognitive science. Mind & Language, 1–19. https://doi.org/10.1111/mila.12364
Coelho Mollo, D., Millière, R., Rathkopf, C., & Stinson, C. (2021). Conceptual Combinations – Benchmark task for Beyond the Imitation Game Benchmark. Github. https://github.com/google/BIG-bench/tree/main/bigbench/benchmark_tasks/conceptual_combinations ; https://www.academia.edu/74816584/Conceptual_Combinations
Demandt, N., Bierbach, D., Kurvers, R. H. J. M., Krause, J., Kurtz, J., & Scharsack, J. P. (2021). Parasite infection impairs the shoaling behaviour of uninfected shoal members under predator attack. Behav Ecol Sociobiol. https://doi.org/https://doi.org/10.1007/s00265-021-03080-7
Driess, D., Ha, J.-S., & Toussaint, M. (2021). Learning to solve sequential physical reasoning problems from a scene image. The International Journal of Robotics Research, 40(12–14), 1435–1466. https://doi.org/10.1177/02783649211056967
Driess, D., Ha, J.-S., Toussaint, M., & Tedrake, R. (2021). Learning Models as Functionals of Signed-Distance Fields for Manipulation Planning. CoRL 2021.
Gu, C., Learned-Miller, E., Gallego, G., Sheldon, D., & Bideau, P. (2021). The Spatio-Temporal Poisson Point Process: A Simple Model for the Alignment of Event Camera Data. arXiv:2106.06887 [cs].
Gu, C., Learned-Miller, E., Gallego, G., Sheldon, D., & Bideau, P. (2021). The Spatio-Temporal Poisson Point process: A simple Model for the Alignment of Event Camera Data. ICCV.
Kahnau, P., Guenther, A., Boon, M. N., Terzenbach, J. D., Hanitzsch, E., Lewejohann, L., & Brust, V. (2021). Lifetime Observation of Cognition and Physiological Parameters in Male Mice. Frontiers in Behavioral Neuroscience, 15, 709775. https://doi.org/10.3389/fnbeh.2021.709775
Kirtay, M., Oztop, E., Asada, M., & Hafner, V. V. (2021). Modeling robot trust based on emergent emotion in an interactive task. 2021 IEEE International Conference on Development and Learning (ICDL), 1–8. https://doi.org/10.1109/ICDL49984.2021.9515645
Kirtay, M., Chevalère, J., Lazarides, R., & Hafner, V. V. (2021). Learning in Social Interaction: Perspectives from Psychology and Robotics. 2021 IEEE International Conference on Development and Learning (ICDL), 1–8. https://doi.org/10.1109/ICDL49984.2021.9515648
Kirtay, M., Oztop, E., Asada, M., & Hafner, V. V. (2021). Trust me! I am a robot: an affective computational account of scaffolding in robot-robot interaction. 2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN), 189–196. https://doi.org/10.1109/RO-MAN50785.2021.9515494
Klamser, P. P., Gómez-Nava, L., Landgraf, T., Jolles, J. W., Bierbach, D., & Romanczuk, P. (2021). Impact of Variable Speed on Collective Movement of Animal Groups. arXiv:2106.00959 [physics, q-bio].
Kurvers, R. H. J. M., Herzog, S. M., Hertwig, R., Krause, J., & Wolf, M. (2021). Pooling decisions decreases variation in response bias and accuracy. IScience, 24(7), 102740. https://doi.org/10.1016/j.isci.2021.102740
Laskowski, K. L., Seebacher, F., Habedank, M., Meka, J., & Bierbach, D. (2021). Two Locomotor Traits Show Different Patterns of Developmental Plasticity Between Closely Related Clonal and Sexual Fish. Frontiers in Physiology, 12, 740604. https://doi.org/10.3389/fphys.2021.740604
Lazarides, R., & Chevalère, J. (2021). Artificial intelligence and education: Addressing the variability in learners' emotion and motivation with adaptive teaching assistants. Bildung Und Erziehung, 74(3), 264–279. https://doi.org/10.13109/buer.2021.74.3.264
Lazarides, R., & Raufelder, D. (2021). Control-value theory in the context of teaching: does teaching quality moderate relations between academic self-concept and achievement emotions? British Journal of Educational Psychology, 91(1), 127–147. https://doi.org/10.1111/bjep.12352
Leonhardt, A., Maier, M., & Rahman, R. A. (2021). The impact of affective knowledge on the perception and evaluation of robot faces. 5th Virtual Social Interactions (VSI) Conference.
Lu, Y., Bierbach, D., Ormanns, J., Warren, W. C., Walter, R. B., & Schartl, M. (2021). Fixation of allelic gene expression landscapes and expression bias pattern shape the transcriptome of the clonal Amazon molly. Genome Research. https://doi.org/doi: 10.1101/gr.268870.120
Lukas, J., Kalinkat, G., Miesen, F. W., Landgraf, T., Krause, J., & Bierbach, D. (2021). Consistent Behavioral Syndrome Across Seasons in an Invasive Freshwater Fish. Frontiers in Ecology and Evolution, 8. https://doi.org/10.3389/fevo.2020.583670
Lukas, J., Romanczuk, P., Klenz, H., Klamser, P., Arias Rodriguez, L., Krause, J., & Bierbach, D. (2021). Acoustic and visual stimuli combined promote stronger responses to aerial predation in fish. Behavioral Ecology, arab043.
Lukas, J., Auer, F., Goldhammer, T., Krause, J., Romanczuk, P., Klamser, P., Arias-Rodriguez, L., & Bierbach, D. (2021). Diurnal Changes in Hypoxia Shape Predator-Prey Interaction in a Bird-Fish System. Frontiers in Ecology and Evolution, 9. https://doi.org/10.3389/fevo.2021.619193
Mieske, P., Diederich, K., & Lewejohann, L. (2021). Roaming in a Land of Milk and Honey: Life Trajectories and Metabolic Rate of Female Inbred Mice Living in a Semi Naturalistic Environment. Animals, 11(10), 3002. https://doi.org/10.3390/ani11103002
Mühlhoff, R. (2021). Predictive Privacy: Towards an Applied Ethics of Data Analytics. Ethics and Information Technology. https://doi.org/10.1007/s10676-021-09606-x
Páll, E., & Brock, O. (2021). Analysis of Open-Loop Grasping From Piles. IEEE International Conference on Robotics and Automation (ICRA), 2591–2597. https://doi.org/10.1109/ICRA48506.2021.9561065
Pannen, T. J., Puhlmann, S., & Brock, O. (2021). A Low-Cost, Easy-to-Manufacture, Flexible, Multi-Taxel Tactile Sensor and its Application to In-Hand Object Recognition. International Conference on Robotics and Automation (ICRA). https://doi.org/https://doi.org/10.48550/arXiv.2111.09687
Pischedda, D., Lange, A., Kirtay, M., Wudarczyk, O. A., Abdel Rahman, R., Hafner, V. V., Kuhlen, A. K., & Haynes, J.-D. (2021). Am I speaking to a human, a robot, or a computer? Neural representations of task partners in communicative interactions with humans or artificial agents. Neuroscience 2021.
Pischedda, D., Lange, A., Kirtay, M., Wudarczyk, O. A., Abdel Rahman, R., Hafner, V. V., Kuhlen, A. K., & Haynes, J.-D. (2021). Who is my interlocutor? Partner-specific neural representations during communicative interactions with human or artificial task partners. 5th Virtual Social Interactions (VSI) Conference.
Pütz, O. (2021). Managing exactness and vagueness in computer science work: Programming and self-repair in meetings. Social Studies of Science, 51(6), 938–961. https://doi.org/https://doi.org/10.1177%2F03063127211010972
Rolfs, M., & Schweitzer, R. (2021). Coupling perception to action through incidental sensory consequences of motor behaviour. Nature Reviews Psychology, 1, 112–123. https://doi.org/10.1038/s44159-021-00015-x
Roth, N., Bideau, P., Hellwich, O., Rolfs, M., & Obermayer, K. (2021). A modular framework for object-based saccadic decisions in dynamic scenes. CVPR EPIC Workshop / arXiv:2106.06073. https://doi.org/10.48550/arXiv.2106.06073
Roth, N., Bideau, P., Hellwich, O., Rolfs, M., & Obermayer, K. (2021). Modeling the influence of objects on saccadic decisions in dynamic real-world scenes. PERCEPTION / 43rd European Conference on Visual Perception (ECVP) 2021.
Schubert, I., Driess, D., Oguz, O. S., & Toussaint, M. (2021). Learning to Execute: Efficient Learning of Universal Plan-Conditioned Policies in Robotics. NeurIPS 2021.
Schweitzer, R., & Rolfs, M. (2021). Intrasaccadic motion streaks jump-start gaze correction. Science Advances, 7(30), eabf2218. https://doi.org/10.1126/sciadv.abf2218
Spatola, N., Chevalère, J., & Lazarides, R. (2021). Human vs computer: What effect does the source of information have on cognitive performance and achievement goal orientation? Paladyn, Journal of Behavioral Robotics, 12(1), 175–186. https://doi.org/10.1515/pjbr-2021-0012
Weber, L., Sänger, M., Garda, S., Barth, F., Alt, C., & Leser, U. (2021). Humboldt@ DrugProt: Chemical-Protein Relation Extraction with Pretrained Transformers and Entity Descriptions. Proceedings of the BioCreative VII challenge evaluation workshop.
Wudarczyk, O. A., Kirtay, M., Kuhlen, A. K., Abdel Rahman, R., Haynes, J.-D., Hafner, V. V., & Pischedda, D. (2021). Bringing Together Robotics, Neuroscience, and Psychology: Lessons Learned From an Interdisciplinary Project. Frontiers in Human Neuroscience, 15. https://doi.org/10.3389/fnhum.2021.630789
Wudarczyk, O. A., Kirtay, M., Pischedda, D., Hafner, V. V., Haynes, J.-D., Kuhlen, A. K., & Abdel Rahman, R. (2021). Robots facilitate human language production. Scientific Reports, 11(1), 16737. https://doi.org/10.1038/s41598-021-95645-9
Yun, H. S., Chevalère, J., Karl, M., & Pinkwart, N. (2021). A comparative study on how social robots support learners' motivation and learning. 14th Annual International Conference of Education, Research and Innovation, 2845–2850. https://doi.org/10.21125/iceri.2021.0708

2020

Andresen, N., Wöllhaf, M., Hohlbaum, K., Lewejohann, L., Hellwich, O., Thöne-Reineke, C., & Belik, V. (2020). Towards a fully automated surveillance of well-being status in laboratory mice using deep learning: Starting with facial expression analysis. PLOS ONE, 15(4), e0228059. https://doi.org/10.1371/journal.pone.0228059
Bastien, R., & Romanczuk, P. (2020). A model of collective behavior based purely on vision. Science Advances, 6(6), eaay0792. https://doi.org/10.1126/sciadv.aay0792
Bierbach, D., Krause, S., Romanczuk, P., Lukas, J., Arias-Rodriguez, L., & Krause, J. (2020). An interaction mechanism for the maintenance of fission–fusion dynamics under different individual densities. PeerJ, 8, e8974. https://doi.org/10.7717/peerj.8974
Bierbach, D., Mönck, H. J., Lukas, J., Habedank, M., Romanczuk, P., Landgraf, T., & Krause, J. (2020). Guppies Prefer to Follow Large (Robot) Leaders Irrespective of Own Size. Frontiers in Bioengineering and Biotechnology, 8. https://doi.org/10.3389/fbioe.2020.00441
Botvinik-Nezer, R., Holzmeister, F., Camerer, C. F., Dreber, A., Huber, J., Johannesson, M., Kirchler, M., Iwanir, R., Mumford, J. A., Adcock, A., others, Pischedda, D., others, & Schonberg, T. (2020). Variability in the analysis of a single neuroimaging dataset by many teams. Nature, 582, 84–88. https://doi.org/10.1038/s41586-020-2314-9
Coelho Mollo, D. (2020). Against Computational Perspectivalism. The British Journal for the Philosophy of Science. https://doi.org/10.1093/bjps/axz036
Halawa, M., Wollhaf, M., Vellasques, E., Sanchez Sanz, U., Urko Sanz, & Hellwich, O. (2020). Learning Disentangled Expression Representations from Facial Images. arxiv.
Jolles, J. W., Weimar, N., Landgraf, T., Romanczuk, P., Krause, J., & Bierbach, D. (2020). Group-level patterns emerge from individual speed as revealed by an extremely social robotic fish. Biology Letters, 16(9), 20200436. https://doi.org/10.1098/rsbl.2020.0436
Kirtay, M., Wudarczyk, O. A., Pischedda, D., Kuhlen, A. K., Abdel Rahman, R., Haynes, J.-D., & Hafner, V. V. (2020). Modeling robot co-representation: state-of-the-art, open issues, and predictive learning as a possible framework. 2020 Joint IEEE 10th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), 1–8. https://doi.org/10.1109/ICDL-EpiRob48136.2020.9278031
Landgraf, T., Gebhardt, G. H. W., Bierbach, D., Romanczuk, P., Musiolek, L., Hafner, V. V., & Krause, J. (2020). Animal-in-the-Loop: Using Interactive Robotic Conspecifics to Study Social Behavior in Animal Groups. Annual Review of Control, Robotics, and Autonomous Systems. https://doi.org/10.1146/annurev-control-061920-103228
Landgraf, T., Moenck, H. J., Gebhardt, G. H. W., Weimar, N., Hocke, M., Maxeiner, M., Musiolek, L., Krause, J., & Bierbach, D. (2020). Socially competent robots: adaptation improves leadership performance in groups of live fish. arXiv:2009.06633 [cs].
Lupyan, G., Abdel Rahman, R., Boroditsky, L., & Clark, A. (2020). Effects of Language on Visual Perception. Trends in Cognitive Sciences, 24(11), 930–944. https://doi.org/10.1016/j.tics.2020.08.005
Mellmann, H., Schlotter, B., Musiolek, L., & Hafner, V. V. (2020). Anticipation as a Mechanism for Complex Behavior in Artificial Agents. Artificial Life Conference Proceedings, 32, 157–159. https://doi.org/10.1162/isal_a_00314
Mühlhoff, R. (2020). Prädiktive Privatheit: Warum wir alle "etwas zu verbergen haben". VerantwortungKI – Künstliche Intelligenz und gesellschaftliche Folgen, herausgegeben von Christoph Markschies und Isabella Hermann. Bd. 3/2020. Berlin-Brandenburgische Akademie der Wissenschaften. https://doi.org/ISBN: 978-3-939818-93-9
Mühlhoff, R. (2020). Automatisierte Ungleichheit: Ethik der Künstlichen Intelligenz in der biopolitische Wende des Digitalen Kapitalismus. Deutsche Zeitschrift Für Philosophie, 6(68). https://doi.org/10.1515/dzph-2020-0059
Musiolek, L., Hafner, V. V., Krause, J., Landgraf, T., & Bierbach, D. (2020). Robofish as Social Partner for Live Guppies. Biomimetic and Biohybrid Systems, 270–274. https://doi.org/10.1007/978-3-030-64313-3_26
Pischedda, D., Palminteri, S., & Coricelli, G. (2020). The effect of counterfactual information on outcome value coding in medial prefrontal and cingulate cortex: From an absolute to a relative neural code. The Journal of Neuroscience, 40(16), 3268–3277. https://doi.org/10.1523/JNEUROSCI.1712-19.2020
Rahmani, P., Peruani, F., & Romanczuk, P. (2020). Flocking in complex environments—Attention trade-offs in collective information processing. PLOS Computational Biology, 16(4), e1007697. https://doi.org/10.1371/journal.pcbi.1007697
Tump, A. N., Pleskac, T. J., & Kurvers, R. H. J. M. (2020). Wise or mad crowds? The cognitive mechanisms underlying information cascades. Science Advances, 6(29), eabb0266. https://doi.org/10.1126/sciadv.abb0266
Winklmayr, C., Kao, A. B., Bak-Coleman, J. B., & Romanczuk, P. (2020). The wisdom of stalemates: consensus and clustering as filtering mechanisms for improving collective accuracy. Proceedings of the Royal Society B: Biological Sciences, 287(1938), 20201802. https://doi.org/10.1098/rspb.2020.1802
Yun, H. S., Karl, M., & Fortenbacher, A. (2020). Designing an interactive second language learning scenario: a case study of Cozmo. Proceedings of HCI Korea, 384–387.
Yun, H. S., Fortenbacher, A., Geißler, S., & Heumos, T. (2020). Towards External Regulation of Emotions Using Sensors: Tow Case Studies. INTED2020, 9313–9320. https://doi.org/10.21125/inted.2020.2576
Zöller, G., Wall, V., & Brock, O. (2020). Active Acoustic Contact Sensing for Soft Pneumatic Actuators. 2020 IEEE International Conference on Robotics and Automation (ICRA), 7966–7972. https://doi.org/10.1109/ICRA40945.2020.9196916

2019

Daniels, B. C., & Romanczuk, P. (2019). Quantifying the impact of network structure on speed and accuracy in collective decision-making. arXiv:1903.09710 [cs, q-bio].
Galbusera, L., Finn, M. T. M., Tschacher, W., & Kyselo, M. (2019). Interpersonal synchrony feels good but impedes self-regulation of affect. Scientific Reports, 9(1), 14691. https://doi.org/10.1038/s41598-019-50960-0
Kurvers, R. H. J. M., Herzog, S. M., Hertwig, R., Krause, J., Moussaid, M., Argenziano, G., Zalaudek, I., Carney, P. A., & Wolf, M. (2019). How to detect high-performing individuals and groups: Decision similarity predicts accuracy. Science Advances, 5(11), eaaw9011. https://doi.org/10.1126/sciadv.aaw9011
Morik, M., Rastogi, D., Jonschkowski, R., & Brock, O. (2019). State Representation Learning with Robotic Priors for Partially Observable Environments. 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 6693–6699. https://doi.org/10.1109/IROS40897.2019.8967938
Sosna, M. M. G., Twomey, C. R., Bak-Coleman, J., Poel, W., Daniels, B. C., Romanczuk, P., & Couzin, I. D. (2019). Individual and collective encoding of risk in animal groups. Proceedings of the National Academy of Sciences, 116(41), 20556–20561. https://doi.org/10.1073/pnas.1905585116
Wall, V., & Brock, O. (2019). Multi-Task Sensorization of Soft Actuators Using Prior Knowledge. 2019 International Conference on Robotics and Automation (ICRA), 9416–9421. https://doi.org/10.1109/ICRA.2019.8793697