Autonomous Robotics

Our research in autonomous robotics is aimed at demonstrating that neural dynamic architectures of embodied cognition can generate object-oriented actions and simple forms of cognition. We organize the work around a scenario in which a partially autonomous robot system interacts with human operators with whom they share a natural environment. The robot system must acquire scene understanding to interpret user commands and autonomously perform actions such as orienting toward objects, retrieving them, possibly manipulating them and handing them over to the human operator. Based on analogies with how nervous systems generate motor behavior and simple forms of cognition, we use attractor dynamics and their instabilities at three levels to generate movement trajectories, to generate goal-directed sequences of behaviors, and to derive task-relevant perceptual representations that support goal-directed behavior.

Interested in autonomous robotics?

Additional material, exercises, and software related to our research topics can be found on the external website related to our theoretical framework Dynamic Field Theory.

If you are a RUB student interested in our work, have a look at the lecture Autonomous Robotics: Action, Perception, and Cognition, or our lab course in autonomous robotics, found under "Teaching" on the left.

We also offer group study projects, as well as Bachelor, Master, and Diploma projects for students of various fields. Check the offered projects under "Teaching" or just contact our group leader with your needs and we will talk about possible projects.

If you would like to visit the lab, meet some of the people, and have a look at our robots, just send an email to our group leader.

For external students and researchers, we offer a yearly summer school on our methods, see the Dynamic Field Theory web pages for more information. 

 

Group Leader

Prof. Dr. Gregor Schöner

Members

Raul Grieben, M.Sc.

Dr.-Ing. Jean-Stephane Jokeit

Jun.-Prof. Dr. Hendrik Reimann

Dr.-Ing. Mathis Richter

Stephan Sehring, M.Sc.

Dr.-Ing. Jan Tekülve

Former Members

Dr. Oliver Lomp

Dr. Yulia Sandamirskaya

Dr. Claudius Strub

    2022

  • A Neural Dynamic Model Perceptually Grounds Nested Noun Phrases
    Sabinasz, D., & Schöner, G.
    Topics in Cognitive Science
    A Neural Dynamic Model Perceptually Grounds Nested Noun Phrases
  • Bridging DFT and DNNs: A neural dynamic process model of scene representation, guided visual search and scene grammar in natural scenes
    Grieben, R., & Schöner, G.
    In J. Culbertson, Perfors, A., Rabagliati, H., & Ramenzoni, V. (Eds.), Proceedings of the 44th Annual Conference of the Cognitive Science Society
    preprint
  • A Perceptually Grounded Neural Dynamic Architecture Establishes Analogy Between Visual Object Pairs
    Hesse, M., Sabinasz, D., & Schöner, G.
    In J. Culbertson, Perfors, A., Rabagliati, H., & Ramenzoni, V. (Eds.), Proceedings of the 44th Annual Conference of the Cognitive Science Society
    A Perceptually Grounded Neural Dynamic Architecture Establishes Analogy Between Visual Object Pairs
  • A Neural Dynamic Model Perceptually Grounds Nested Noun Phrases
    Sabinasz, D., & Schöner, G.
    In J. Culbertson, Perfors, A., Rabagliati, H., & Ramenzoni, V. (Eds.), Proceedings of the 44th Annual Conference of the Cognitive Science Society
    A Neural Dynamic Model Perceptually Grounds Nested Noun Phrases

    • 2021

  • How do neural processes give rise to cognition? Simultaneously predicting brain and behavior with a dynamic model of visual working memory.
    Buss, A. T., Magnotta, V. A., Penny, W., Schöner, G., Huppert, T. J., & Spencer, J. P.
    Psychological Review, 128(2), 362–395
    preprint
  • A neural dynamic process model of combined bottom-up and top-down guidance in triple conjunction visual search
    Grieben, R., & Schöner, G.
    In T. Fitch, Lamm, C., Leder, H., & Teßmar-Raible, K. (Eds.), Proceedings of the 43rd Annual Conference of the Cognitive Science Society
    preprint
  • A Neural Dynamic Model of the Perceptual Grounding of Spatial and Movement Relations
    Richter, M., Lins, J., & Schöner, G.
    Cognitive Science, 45(10), e13045
    Paper (open publication) Link to Journal

    • 2020

  • Motor Habituation: Theory and Experiment
    Aerdker, S., Feng, J., & Schöner, G.
    10th Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob 2020) pp. 160-167
    Motor Habituation: Theory and Experiment (pdf)
  • Scene memory and spatial inhibition in visual search: A neural dynamic process model and new experimental evidence
    Grieben, R., Tekülve, J., Zibner, S. K. U., Lins, J., Schneegans, S., & Schöner, G.
    Attention, Perception, & Psychophysics
    SpringerLink (Open Access)
  • Grounding Spatial Language in Perception by Combining Concepts in a Neural Dynamic Architecture
    Sabinasz, D., Richter, M., Lins, J., & Schöner, G.
    In S. Denison, Mack, M., Xu, Y., & Armstrong, B. C. (Eds.), Proceedings of the 42nd Annual Conference of the Cognitive Science Society (pp. 620–626) Cognitive Science Society
    pdf

    • 2019

  • Computer mouse tracking reveals motor signatures in a cognitive task of spatial language grounding
    Lins, J., & Schöner, G.
    Attention, Perception, & Psychophysics
    pdf
  • A process account of the UnControlled Manifold uncontrolled manifold structure of joint space variance in pointing movements
    Martin, V., Reimann, H., & Schöner, G.
    Biological Cybernetics
    reprint
  • The Dynamics of Neural Populations Capture the Laws of the Mind
    Schöner, G.
    Topics in Cognitive Science, 1–15
    reprint
  • Autonomous Sequence Generation for a Neural Dynamic Robot: Scene Perception, Serial Order, and Object-Oriented Movement
    Tekülve, J., Fois, A., Sandamirskaya, Y., & Schöner, G.
    Frontiers in Neurorobotics, 13, 95
    Article
  • Neural dynamic concepts for intentional systems
    Tekülve, J., & Schöner, G.
    In 41th Annual Conference of the Cognitive Science Society (CogSci 2019)
    Neural dynamic concepts for intentional systems (Preprint)
  • Autonomously learning beliefs is facilitated by a neural dynamic network driving an intentional agent
    Tekülve, J., & Schöner, G.
    In Development and Learning and Epigenetic Robotics (ICDL-EpiRob), 2019 Joint IEEE International Conference on (pp. 143–150) IEEE
    pdf cedar architecture files

    • 2018

  • A neural dynamic model for the perceptual grounding of spatial and movement relations
    Richter, M.
    Doctoral thesis, Ruhr-Universität Bochum, Universitätsstr. 150, 44801 Bochum
    pdf
  • Sequences of discrete attentional shifts emerge from a neural dynamic architecture for conjunctive visual search that operates in continuous time
    Grieben, R., Tekülve, J., Zibner, S. K. U., Schneegans, S., & Schöner, G.
    In T. T. Rogers, Rau, M., Zhu, X., & Kalish, C. W. (Eds.), Proceedings of the 40th Annual Conference of the Cognitive Science Society
    preprint
  • Anticipatory coarticulation in non-speeded arm movements can be motor-equivalent, carry-over coarticulation always is
    Hansen, E., Grimme, B., Reimann, H., & Schöner, G.
    Experimental Brain Research
    reprint
  • Erste Ansätze zur automatischen Erkennung von Gruppenverhalten mithilfe des Computersehens
    Horn, D., Houben, S., & Schöner, G.
    In J. Reichertz & Keysers, V. (Eds.), Emotion. Eskalation. Gewalt. (pp. 130–147) Beltz Juventa
  • A Neural Dynamic Architecture That Autonomously Builds Mental Models
    Kounatidou, P., Richter, M., & Schöner, G.
    In T. T. Rogers, Rau, M., Zhu, X., & Kalish, C. W. (Eds.), Proceedings of the 40th Annual Conference of the Cognitive Science Society (pp. 643–648)
    preprint

    • 2017

  • A Neural Dynamic Architecture for Reaching and Grasping Integrates Perception and Movement Generation and Enables On-Line Updating
    Knips, G., Zibner, S. K. U., Reimann, H., & Schöner, G.
    Frontiers in Neurorobotics, 11(March), 9:1–14
    reprint
  • Cue Integration by Similarity Rank List Coding — Application to Invariant Object Recognition
    Grieben, R., & Würtz, R. P.
    In Proceedings of IEEE International Workshops on Foundations and Applications of Self* Systems (pp. 132–137)
    pdf
  • Mouse Tracking Shows Attraction to Alternative Targets While Grounding Spatial Relations
    Lins, J., & Schöner, G.
    In Proceedings of the 39th Annual Conference of the Cognitive Science Society Austin, TX: Cognitive Science Society
  • A Neural-Dynamic Architecture for Concurrent Estimation of Object Pose and Identity
    Lomp, O., Faubel, C., & Schöner, G.
    Frontiers in Neurorobotics, 11(April), 23
    reprint
  • A multi-joint model of quiet , upright stance accounts for the “uncontrolled manifold”-structure of joint variance
    Reimann, H., & Schöner, G.
    Biological Cybernetics, in press
    preprint
  • A neural dynamic model generates descriptions of object-oriented actions
    Richter, M., Lins, J., & Schöner, G.
    Topics in Cognitive Science, 9(1), 35–47
    A neural dynamic model generates descriptions of object-oriented actions (PDF)
  • Reaching for objects : a neural process account in a developmental perspective
    Schöner, G., Tekülve, J., & Zibner, S.
    In D. Corbetta & Santello, M. (Eds.), The selection and production of goal-directed behaviors: Neural correlates, development, learning, and modeling of reach-to-grasp movements Taylor & Francis
    preprint
  • Dynamic Neural Fields with Intrinsic Plasticity
    Strub, C., Schöner, G., Wörgötter, F., & Sandamirskaya, Y.
    Frontiers in Computational Neuroscience, 11(August), 74
    reprint

    • 2016

  • Temporal Asymmetry in Dark–Bright Processing Initiates Propagating Activity across Primary Visual Cortex
    Rekauzke, S., Nortmann, N., Staadt, R., Hock, H. S., Schöner, G., & Jancke, D.
    The Journal of Neuroscience, 36(6), 1902–1913
    pdf
  • Nonlinear dynamics in the perceptual grouping of connected surfaces
    Hock, H. S., & Schöner, G.
    Vision Research, 126, 80–96
    reprint
  • Developing Dynamic Field Theory Architectures for Embodied Cognitive Systems with cedar
    Lomp, O., Richter, M., Zibner, S. K. U., & Schöner, G.
    Frontiers in Neurorobotics, 10(November), 14
    reprint
  • Coordination of muscle torques stabilizes upright standing posture: an UCM analysis
    Park, E., Reimann, H., & Schöner, G.
    Experimental Brain Research, 234(6), 1757–1767
    preprint
  • Separating Timing, Movement Conditions and Individual Differences in the Analysis of Human Movement
    Raket, L. L., Grimme, B., Schöner, G., Igel, C., & Markussen, B.
    PLoS Computational Biology, 12(9), 1–27
    reprint
  • A neural dynamic model parses object-oriented actions
    Richter, M., Lins, J., & Schöner, G.
    In A. Papafragou, Grodner, D., Mirman, D., & Trueswell, J. C. (Eds.), Proceedings of the 38th Annual Conference of the Cognitive Science Society (pp. 1931–1936) Austin, TX: Cognitive Science Society
    pdf
  • A neural process model of learning to sequentially organize and activate pre-reaches
    Tekülve, J., Zibner, S. K. U., & Schöner, G.
    In Development and Learning and Epigenetic Robotics (ICDL-Epirob), 2016 Joint IEEE International Conferences on
    A neural process model of learning to sequentially organize and activate pre-reaches

    • 2015

  • Carry-over coarticulation in joint angles
    Hansen, E., Grimme, B., Reimann, H., & Schöner, G.
    Experimental Brain Research, 233(9), 2555–2569
    reprint
  • Parsing of action sequences: A neural dynamics approach
    Lobato, D., Sandamirskaya, Y., Richter, M., & Schöner, G.
    Paladyn, Journal of Behavioral Robotics, 6(1), 119–135
    reprint
  • Learning the condition of satisfaction of an elementary behavior in dynamic field theory
    Luciw, M., Kazerounian, S., Lahkman, K., Richter, M., & Sandamirskaya, Y.
    Paladyn, Journal of Behavioral Robotics, 6(1), 180–190
    pdf
  • Task-specific stability of abundant systems: Structure of variance and motor equivalence
    Mattos, D., Schöner, G., Zatsiorsky, V. M., & Latash, M. L.
    Neuroscience, 310, 600–615
    reprint
  • Motor equivalence during multi-finger accurate force production
    Mattos, D., Schöner, G., Zatsiorsky, V. M., & Latash, M. L.
    Experimental Brain Research, 233, 487–502
    pdf
  • The Dynamics of Neural Activation Variables
    Reimann, H., Lins, J., & Schöner, G.
    Paladyn, Journal of Behavioral Robotics, 6(1), 57–70
    pdf
  • Artificial Neural Networks — Methods and Applications in Bio-/Neuroinformatics
    Sandamirskaya, Y., & Storck, T.
    In P. Koprinkova-Hristova, Mladenov, V., & Kasabov, N. K. (Eds.) (Vol. 4) Springer
    pdf
  • The Neural Dynamics of Goal-Directed Arm Movements: A Developmental Perspective
    Zibner, S. K. U., Tekülve, J., & Schöner, G.
    In Development and Learning and Epigenetic Robotics (ICDL-Epirob), 2015 Joint IEEE International Conferences on (pp. 154–161)
    model simulation pdf
  • The Sequential Organization of Movement is Critical to the Development of Reaching: A Neural Dynamics Account
    Zibner, S. K. U., Tekülve, J., & Schöner, G.
    In Development and Learning and Epigenetic Robotics (ICDL-Epirob), 2015 Joint IEEE International Conferences on (pp. 39–46)
    pdf

    • 2014

  • Learning to Look: a Dynamic Neural Fields Architecture for Gaze Shift Generation
    Bell, C., Storck, T., & Sandamirskaya, Y.
    In International Conference for Artificial Neural Networks, ICANN Hamburg, Germany
    pdf
  • A comparison between reactive potential fields and Attractor Dynamics
    Hernandes, A. C., Guerrero, H. B., Becker, M., Jokeit, J. -S., & Schöner, G.
    Circuits and Systems (CWCAS), 2014 IEEE 5th Colombian Workshop on
    reprint
  • A neural dynamics architecture for grasping that integrates perception and movement generation and enables on-line updating
    Knips, G., Zibner, S. K. U., Reimann, H., Popova, I., & Schöner, G.
    In International Conference on Intelligent Robots and Systems (IROS) (pp. 646–653)
  • Reaching and grasping novel objects: Using neural dynamics to integrate and organize scene and object perception with movement generation
    Knips, G., Zibner, S. K. U., Reimann, H., Popova, I., & Schöner, G.
    In International Conference on Development and Learning and on Epigenetic Robotics (ICDL-EPIROB) (pp. 416–423)
  • Neural Fields
    Lins, J., & Schöner, G.
    In S. Coombes, beim Graben, P., Potthast, R., & , J. W. (Eds.) (pp. 319–339) Springer Berlin Heidelberg
    pdf
  • Instance-based object recognition with simultaneous pose estimation using keypoint maps and neural dynamics
    Lomp, O., Terzić, K., Faubel, C., du Buf, J. M. H., & Schöner, G.
    In International Conference on Artificial Neural Networks (pp. 451–458) Springer
  • Reinforcement-Driven Shaping of Sequence Learning in Neural Dynamics
    Luciw, M., Kazerounian, S., Sandamirskaya, Y., Schöner, G., & Schmidhuber, J.
    In Simulation of Adaptive Behavior, SAB
    pdf
  • Change occurs when body meets environment: A review of the embodied nature of development
    Maruyama, S., Dineva, E., Spencer, J. P., & Schöner, G.
    Japanese Psychological Research, 56, 385–401
    pdf
  • Contrasting accounts of direction and shape perception in short-range motion: Counterchange compared with motion energy detection.
    Norman, J., Hock, H., & Schoner, G.
    Attention, perception & psychophysics, 76, 1350–70
    pdf
  • A neural dynamics to organize timed movement : Demonstration in a robot ball bouncing task
    Oubbati, F., Richter, M., & Schöner, G.
    In 4th International Conference on Development and Learning and on Epigenetic Robotics (pp. 291–298) Palazzo Ducale, Genoa, Italy
    reprint
  • Autonomous Neural Dynamics to Test Hypotheses in a Model of Spatial Language
    Richter, M., Lins, J., Schneegans, S., Sandamirskaya, Y., & Schöner, G.
    In P. Bello, Guarini, M., McShane, M., & Scassellati, B. (Eds.), Proceedings of the 36th Annual Conference of the Cognitive Science Society (pp. 2847–2852) Austin, TX: Cognitive Science Society
    pdf
  • A neural dynamic architecture resolves phrases about spatial relations in visual scenes
    Richter, M., Lins, J., Schneegans, S., & Schöner, G.
    In 24th International Conference on Artificial Neural Networks (ICANN) (pp. 201–208) Heidelberg, Germany: Springer
    pdf
  • Neural-Dynamic Architecture for Looking: Shift from Visual to Motor Target Representation for Memory Saccade
    Sandamirskaya, Y., & Storck, T.
    In IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL EPIROB 2014)
    pdf
  • Dynamic interactions between visual working memory and saccade target selection
    Schneegans, S., Spencer, J. P., Schöner, G., Hwang, S., & Hollingworth, A.
    Journal of vision, 14(11), 9
    supplementary material pdf
  • Use of the Uncontrolled Manifold (UCM) Approach to Understand MotorVariability, Motor Equivalence, and Self-motion
    Scholz, J. P., & Schöner, G.
    In M. F. Levin (Ed.), Progress in Motor Control (Vol. 826, p. Chapter 7) Springer International Publishing
    preprint
  • Embodied Cognition, Neural Field Models of
    Schöner, G.
    In Encyclopedia of Computational Neuroscience (pp. 1084–1092) Springer Berlin Heidelberg
    preprint
  • Dynamical Systems Thinking: From Metaphor to Neural Theory
    Schöner, G.
    In P. C. M. Molenaar, Lerner, R. M., & Newell, K. M. (Eds.), Handbook of Developmental Systems Theory and Methodology (pp. 188–219) New York, New York, USA: Guilford Publications
    preprint
  • Coordination Dynamics
    Schöner, G., & Nowak, E.
    In D. Jaeger & Jung, R. (Eds.), Encyclopedia of Computational Neuroscience (pp. 1–3) New York, NY: Springer New York
    preprint
  • Correcting Pose Estimates during Tactile Exploration of Object Shape: a Neuro-robotic Study
    Strub, C., Wörgötter, F., Ritter, H., & Sandamirskaya, Y.
    In Development and Learning and Epirobotics (ICDL-Epirob), IEEE International Conference on
    pdf
  • Using Haptics to Extract Object Shape from Rotational Manipulations
    Strub, C., Wörgötter, F., Ritter, H., & Sandamirskaya, Y.
    In Intelligent Robots and Systems (IROS), IEEE/RSJ International Conference on IEEE
    pdf

    • 2013

  • Autonomous robot hitting task using dynamical system approach
    Oubbati, F., Richter, M., & Schöner, G.
    In IEEE International Conference on Systems, Man, and Cybernetics (pp. 4042–4047) IEEE
    pdf
  • Autonomous Reinforcement of Behavioral Sequences in Neural Dynamics
    Kazerounian, S., Luciw, M., Richter, M., & Sandamirskaya, Y.
    In International Joint Conference on Neural Networks (IJCNN)
    pdf
  • A software framework for cognition, embodiment, dynamics, and autonomy in robotics: cedar
    Lomp, O., Zibner, S. K. U., Richter, M., Ranó, I., & Schöner, G.
    In International Conference on Artificial Neural Networks (pp. 475–482) Springer
    pdf
  • Learning the Perceptual Conditions of Satisfaction of Elementary Behaviors
    Luciw, M., Kazerounian, S., Lakhmann, K., Richter, M., & Sandamirskaya, Y.
    In Robotics: Science and Systems (RSS), Workshop "Active Learning in Robotics: Exploration, Curiosity, and Interaction"
    pdf
  • Dynamic Neural Fields as a Step Towards Cognitive Neuromorphic Architectures
    Sandamirskaya, Y.
    Frontiers in Neuroscience, 7, 276
    pdf
  • Increasing Autonomy of Learning SensorimotorTransformations with Dynamic Neural Fields
    Sandamirskaya, Y., & Conradt, J.
    In International Conference on Robotics and Automation (ICRA), Workshop "Autonomous Learning"
    pdf
  • Learning Sensorimotor Transformations with Dynamic Neural Fields
    Sandamirskaya, Y., & Conradt, J.
    In International Conference on Artificial Neural Networks (ICANN)
    pdf
  • Using Dynamic Field Theory to Extend the Embodiment Stance toward Higher Cognition
    Sandamirskaya, Y., Zibner, S. K. U., Schneegans, S., & Schöner, G.
    New Ideas in Psychology, 31(3), 322–339
    pdf

    • 2012

  • Autonomous reinforcement of behavioral sequences in neural dynamics
    Kazerounian, S., Luciw, M., Sandamirskaya, Y., Richter, M., Schmidhuber, J., & Schöner, G.
    In IEEE International Conference on Development and Learning and Epigenetic Robotics (Vol. 1, pp. 1–2) Ieee
    pdf
  • Naturalistic arm movements during obstacle avoidance in 3D and the identification of movement primitives.
    Grimme, B., Lipinski, J., & Schöner, G.
    Experimental brain research, 222(3), 185–200
    pdf
  • Neural Dynamics of Hierarchically Organized Sequences: a Robotic Implementation
    Duran,, & Sandamirskaya, Y.
    In Proceedings of 2012 IEEE-RAS International Conference on Humanoid Robots (Humanoids)
    pdf
  • A Dynamic Field Architecture for the Generation of Hierarchically Organized Sequences
    Duran, B., Sandamirskaya, Y., & Schöner, G.
    In A. E. P. Villa, Duch, W., Érdi, P., Masulli, F., & Palm, G. (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2012 (Vol. 7552, pp. 25–32) Springer Berlin Heidelberg
    pdf
  • Sensorimotor Learning Biases Choice Behavior: A Learning Neural Field Model for Decision Making
    Klaes, C., Schneegans, S., Schöner, G., & Gail, A.
    PLoS computational biology, 8(11), e1002774
    supplementary material pdf
  • The Function and Fallibility of Visual Feature Integration: A Dynamic Neural Field Model of Illusory Conjunctions
    Lins, J., Schneegans, S., Spencer, J., & Schöner, G.
    Frontiers in Computational Neuroscience, (128)
    pdf
  • A Neuro-Behavioral Model of Flexible Spatial Language Behaviors
    Lipinski, J., Schneegans, S., Sandamirskaya, Y., Spencer, J. P., & Schöner, G.
    Journal of Experimental Psychology: Learning, Memory and Cognition., 38(6), 1490–1511
    pdf
  • Functional synergies underlying control of upright posture during changes in head orientation
    Park, E., Schöner, G., & Scholz, J. P.
    PLoS ONE, 7(8), 1–12
    pdf
  • A robotic architecture for action selection and behavioral organization inspired by human cognition
    Richter, M., Sandamirskaya, Y., & Schöner, G.
    In IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS
    pdf
  • A neural mechanism for coordinate transformation predicts pre-saccadic remapping
    Schneegans, S., & Schöner, G.
    Biological cybernetics, 106(2), 89–109
    pdf
  • How visual information links to multijoint coordination during quiet standing
    Scholz, J. P., Park, E., Jeka, J. J., Schöner, G., & Kiemel, T.
    Experimental Brain Research, 222, 229–239
    pdf
  • A neural-dynamic architecture for flexible spatial language: intrinsic frames, the term “between”, and autonomy
    van Hengel, U., Sandamirskaya, Y., Schneegans, S., & Schöner, G.
    In 21st IEEE International Symposium on Robot and Human Interactive Communication (Ro-Man) 2012 (pp. 150–157)
    pdf

    • 2011

  • The temporal dynamics of global-to-local feedback in the formation of hierarchical motion patterns: psychophysics and computational simulations.
    Hock, H. S., Schöner, G., Brownlow, S., & Taler, D.
    Attention, perception & psychophysics, 73(4), 1171–94
    pdf
  • Limb versus speech motor control: a conceptual review.
    Grimme, B., Fuchs, S., Perrier, P., & Schöner, G.
    Motor control, 15(1), 5–33
    pdf
  • Autonomous movement generation for manipulators with multiple simultaneous constraints using the attractor dynamics approach
    Reimann, H., Iossifidis, I., & Schöner, G.
    In 2011 IEEE International Conference on Robotics and Automation, ICRA2011
  • A neural-dynamic architecture for behavioral organization of an embodied agent
    Sandamirskaya, Y., Richter, M., & Schöner, G.
    In IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL EPIROB 2011) (pp. 1–7)
    pdf
  • Motor equivalence and self-motion induced by different movement speeds
    Scholz, J. P., Dwight-Higgin, T., Lynch, J. E., Tseng, Y. -W., Martin, V., & Schöner, G.
    Experimental Brain Research, 209(3), 319–332
    pdf
  • Dynamic Neural Fields as Building Blocks of a Cortex-Inspired Architecture for Robotic Scene Representation
    Zibner, S. K. U., Faubel, C., Iossifidis, I., & Schöner, G.
    IEEE Transactions on Autonomous Mental Development, 3(1), 74–91
    pdf
  • Making a robotic scene representation accessible to feature and label queries
    Zibner, S. K. U., Faubel, C., & Schöner, G.
    In Proceedings of the First Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL EPIROB 2011)

    • 2010

  • Motor Abundance Contributes to Resolving Multiple Kinematic Task Constraints
    Gera, G., Freitas, S., Latash, M., Monahan, K., Schöner, G., & Scholz, J.
    Motor Control, 14, 83–115
    pdf
  • Measuring Perceptual Hysteresis with the Modified Method of Limits: Dynamics at the Threshold
    Hock, H. S., & Schöner, G.
    Seeing and Perceiving, 23, 173–195
    pdf
  • Motor control theories and their applications
    Latash, M., Levin, M. F., Scholz, J. P., & Schöner, G.
    Medicina (Kaunas), 29(6), 997–1003
    pdf
  • Natural human-robot interaction through spatial language: a dynamic neural fields approach
    Sandamirskaya, Y., Lipinski, J., Iossifidis, I., & Schöner, G.
    In 19th IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN (pp. 600–607) Viareggio, Italy
    pdf
  • An embodied account of serial order: how instabilities drive sequence generation
    Sandamirskaya, Y., & Schöner, G.
    Neural Networks, 23(10), 1164–1179
    pdf
  • Serial order in an acting system: a multidimensional dynamic neural fields implementation
    Sandamirskaya, Y., & Schöner, G.
    In Development and Learning, 2010. ICDL 2010. 9th IEEE International Conference on
    pdf
  • Scene Representation for Anthropomorphic Robots: A Dynamic Neural Field Approach
    Zibner, S. K. U., Faubel, C., Iossifidis, I., & Schöner, G.
    In ISR / ROBOTIK 2010 Munich, Germany
  • Scenes and tracking with dynamic neural fields: How to update a robotic scene representation
    Zibner, S. K. U., Faubel, C., Iossifidis, I., Schöner, G., & Spencer, J. P.
    In Development and Learning (ICDL), 2010 IEEE 9th International Conference on (pp. 244–250) IEEE

    • 2009

  • A layered neural architecture for the consolidation, maintenance, and updating of representations in visual working memory.
    Johnson, J. S., Spencer, J. P., & Schöner, G.
    Brain research, 1299, 17–32
    pdf
  • A layered neural architecture for the consolidation, maintenance, and updating of representations in visual working memory.
    Johnson, J. S., Spencer, J. P., & Schöner, G.
    Brain research, 1299, 17–32
    pdf
  • A counterchange mechanism for the perception of motion.
    Hock, H. S., Schöner, G., & Gilroy, L.
    Acta psychologica, 132(1), 1–21
    pdf
  • Swing it to the Left, Swing it to the Right: Enacting Flexible Spatial Language Using a Neurodynamic Framework
    Lipinski, J., Sandamirskaya, Y., & Schöner, G.
    Cognitive Neurodynamics, 3(4)
    pdf
  • An Integrative Framework for Spatial Language and Color: Robotic Demonstrations Using the Dynamic Field Theory.
    Lipinski, J., Sandamirskaya, Y., & Schöner, G.
    In 31th Annual Meeting of the Cognitive Science Society, CogSci 2009 Amstredam, NL
    pdf
  • Behaviorally Flexible Spatial Communication: Robotic Demonstrations of a Neurodynamic Framework
    Lipinski, J., Sandamirskaya, Y., & Schöner, G.
    In B. Mertsching, Hund, M., & Z., A. (Eds.), KI 2009, Lecture Notes in Artificial Intelligence (Vol. 5803, pp. 257–264) Berlin: Springer-Verlag
    pdf
  • Redundancy, self-motion and motor control
    Martin, V., Scholz, J. P., & Schöner, G.
    Neural Computation, 21(5), 1371–1414
    pdf
  • Temporal stabilization of discrete movement in variable environments: an attractor dynamics approach
    Tuma, M., Iossifidis, I., & Schöner, G.
    In IEEE International Conference on Robotics and Automation (ICRA) (pp. 863–868)
    pdf

    • 2008

  • Dynamic Field Theory as a framework for understanding embodied cognition
    Schneegans, S., & Schöner, G.
    In P. Calvo & Gomila, T. (Eds.), Handbook of cognitive science: An embodied approach (pp. 241–271) Amsterdam, Netherlands: Elsevier
    pdf

    • 2004

  • Shorter latencies for motion trajectories than for flashes in population responses of cat primary visual cortex
    Jancke, D., Erlhagen, W., Schöner, G., & Dinse, H. R.
    The Journal of Physiology, 556(3), 971–982

    • 2002

  • Timing, Clocks, and Dynamical Systems
    Schöner, G.
    Brain and Cognition, 48, 31–51
    reprint

    • 2000

  • Target representation on an autonomous vehicle with low-level sensors
    Bicho, E., Mallet, P., & Schöner, G.
    The International Journal of Robotics Research, 19, 424–447
    reprint

    • 1999

  • The distribution of neuronal population activation (DPA) as a tool to study interaction and integration in cortical representations
    Erlhagen, W., Bastian, A., Jancke, D., Riehle, A., & Schöner, G.
    Journal of Neuroscience Methods, 94(1), 53–66
  • Parametric population representation of retinal location: Neuronal interaction dynamics in cat primary visual cortex
    Jancke, D., Erlhagen, W., Dinse, H. R., Akhavan, A. C., Giese, M., Steinhage, A., & Schöner, G.
    J Neurosci, 19(20), 9016–9028

    • 1998

  • Using attractor dynamics to control autonomous vehicle motion
    Bicho, E., Mallet, P., & Schöner, G.
    In Proceedings of IECON′98 (pp. 1176–1181) IEEE Industrial Electronics Society
    reprint

    • 1997

  • The dynamic approach to autonomous robotics demonstrated on a low-level vehicle platform
    Bicho, E., & Schöner, G.
    Robotics and autonomous systems, 21, 23–35
    reprint

    • 1996

  • Population coding in cat visual cortex reveals nonlinear interactions as predicted by a neural field model
    Jancke, D., Akhavan, A. C., Erlhagen, W., Giese, M., Steinhage, A., Schöner, G., & Dinse, H. R.
    In Artificial Neural Networks — ICANN 96 (pp. 641–648) Springer Berlin Heidelberg

    • 1995

  • Dynamics of behavior: Theory and applications for autonomous robot architectures
    Schöner, G., Dose, M., & Engels, C.
    Robotics and Autonomous Systems, 16, 213–245
    SchonerDoseEngels95.pdf

    • 1986

  • A stochastic theory of phase transitions in human hand movement
    Schöner, G., Haken, H., & Kelso, J. A. S.
    Biological Cybernetics, 53, 247–257
    pdf bib

    2017

  • A Neurodynamic Model for Haptic Spatiotemporal Integration
    Strub, C.
    Doctoral thesis, Fakultät für Elektrotechnik und Informationstechnik Ruhr-Universität Bochum
    Dissertation

    • 2016

  • Cognitive object recognition based on dynamic field theory
    Lomp, O.
    Doctoral thesis, Fakultät für Elektrotechnik und Informationstechnik, Ruhr-Universität Bochum
    Dissertation

    • 2015

  • A Neuro-Dynamic Architecture for Autonomous Visual Scene Representation
    Zibner, S. K. U.
    Doctoral thesis, Fakultät für Elektrotechnik und Informationstechnik, Ruhr-Universität Bochum
    Dissertation

    • 2014

  • Autonomous generation and on-line updating of sequences of timed robotic actions: an attractor dynamics approach
    Oubbati, F.
    Doctoral thesis, Fakultät für Elektrotechnik und Informationstechnik Ruhr-Universität Bochum
    Dissertation

    • 2010

  • Sequence generation in Dynamic Field Theory
    Sandamirskaya, Y.
    Doctoral thesis, Fakultät für Physik und Astronomie Ruhr-Universität Bochum

    • 2009

  • Object recognition with dynamic neural fields
    Faubel, C.
    Doctoral thesis, Fakultät für Elektrotechnik und Informationstechnik Ruhr-Universität Bochum

About the INI

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.

Contact

Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany

Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210

sekretariat@ini.rub.de
www.ini.rub.de