Autonomous Robotics

Our research in autonomous robotics is organized around the problems posed by robotic assistants, that is, partially autonomous robot systems that interact with human operators with whom they share a natural environment. Robotic assistants need an array of sensor systems and powerful perceptual algorithms so that they may acquire enough information about the scene 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 of our summer school.

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.

 

Richter, M., Lins, J., & Schöner, G.. (2017). A neural dynamic model generates descriptions of object-oriented actions. Topics in Cognitive Science, 9(1), 35–47. http://doi.org/10.1111/tops.12240
Rekauzke, S., Nortmann, N., Staadt, R., Hock, H. S., Schöner, G., & Jancke, D.. (2016). Temporal asymmetry in dark-bright processing initiates propagating activity across primary visual cortex. J Neurosci, 36(6), 1902–1913.
Tekülve, J., Zibner, S. K. U., & Schöner, G.. (2016). A neural process model of learning to sequentially organize and activate pre-reaches. In Development and Learning and Epigenetic Robotics (ICDL-Epirob), 2016 Joint IEEE International Conferences on.
Mattos, D., Schöner, G., Zatsiorsky, V. M., & Latash, M. L. (2015). Motor equivalence during multi-finger accurate force production. Experimental Brain Research, 233, 487–502. http://doi.org/10.1007/s00221-014-4128-1
Reimann, H., Lins, J., & Schöner, G.. (2015). The Dynamics of Neural Activation Variables. Paladyn, Journal of Behavioral Robotics, 6(1), 57–70.
Sandamirskaya, Y., & Storck, T. (2015). Artificial Neural Networks — Methods and Applications in Bio-/Neuroinformatics. In P. Koprinkova-Hristova, Mladenov, V., & Kasabov, N. K. (Eds.) (Vol. 4). Springer.
Zibner, S. K. U., Tekülve, J., & Schöner, G.. (2015). The Neural Dynamics of Goal-Directed Arm Movements: A Developmental Perspective. In Development and Learning and Epigenetic Robotics (ICDL-Epirob), 2015 Joint IEEE International Conferences on (pp. 154–161).
Zibner, S. K. U., Tekülve, J., & Schöner, G.. (2015). The Sequential Organization of Movement is Critical to the Development of Reaching: A Neural Dynamics Account. In Development and Learning and Epigenetic Robotics (ICDL-Epirob), 2015 Joint IEEE International Conferences on (pp. 39–46).
Lomp, O., Terzić, K., Faubel, C., du Buf, J. M. H., & Schöner, G.. (2014). Instance-based Object Recognition with Simultaneous Pose Estimation Using Keypoint Maps and Neural Dynamics. In S. Wermter, Weber, C., Duch, W., Honkela, T., Koprinkova-Hristova, P. D., , S. M., et al. (Eds.), ICANN 2014 (Vol. 8681, pp. 451–458). Hamburg.
Bell, C., Storck, T., & Sandamirskaya, Y.. (2014). Learning to Look: a Dynamic Neural Fields Architecture for Gaze Shift Generation. In International Conference for Artificial Neural Networks, ICANN. Hamburg, Germany.
Knips, G., Zibner, S. K. U., Reimann, H., Popova, I., & Schöner, G.. (2014). A neural dynamics architecture for grasping that integrates perception and movement generation and enables on-line updating. In International Conference on Intelligent Robots and Systems (IROS) (pp. 646–653).
Knips, G., Zibner, S. K. U., Reimann, H., Popova, I., & Schöner, G.. (2014). Reaching and grasping novel objects: Using neural dynamics to integrate and organize scene and object perception with movement generation. In International Conference on Development and Learning and on Epigenetic Robotics (ICDL-EPIROB) (pp. 416–423).
Lins, J., & Schöner, G.. (2014). Neural Fields. In S. Coombes, beim Graben, P., Potthast, R., & , J. W. (Eds.) (pp. 319–339). Springer Berlin Heidelberg.
Luciw, M., Kazerounian, S., Sandamirskaya, Y., Schöner, G., & Schmidhuber, J. (2014). Reinforcement-Driven Shaping of Sequence Learning in Neural Dynamics. In Simulation of Adaptive Behavior, SAB.
Maruyama, S., Dineva, E., Spencer, J. P., & Schöner, G.. (2014). Change occurs when body meets environment: A review of the embodied nature of development. Japanese Psychological Research, 56, 385–401. http://doi.org/10.1111/jpr.12065
Norman, J., Hock, H., & Schoner, G.. (2014). Contrasting accounts of direction and shape perception in short-range motion: Counterchange compared with motion energy detection. Attention, perception & psychophysics, 76, 1350–70. http://doi.org/10.3758/s13414-014-0650-2
Richter, M., Lins, J., Schneegans, S., Sandamirskaya, Y., & Schöner, G.. (2014). Autonomous Neural Dynamics to Test Hypotheses in a Model of Spatial Language. 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.
Richter, M., Lins, J., Schneegans, S., & Schöner, G.. (2014). A neural dynamic architecture resolves phrases about spatial relations in visual scenes. In 24th International Conference on Artificial Neural Networks (ICANN) (pp. 201–208). Heidelberg, Germany: Springer.
Sandamirskaya, Y., & Storck, T. (2014). Neural-Dynamic Architecture for Looking: Shift from Visual to Motor Target Representation for Memory Saccade. In IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL EPIROB 2014).
Schneegans, S., Spencer, J. P., Schöner, G., Hwang, S., & Hollingworth, A. (2014). Dynamic interactions between visual working memory and saccade target selection. Journal of vision, 14(11), 9.
Strub, C., Wörgötter, F., Ritter, H., & Sandamirskaya, Y.. (2014). Correcting Pose Estimates during Tactile Exploration of Object Shape: a Neuro-robotic Study. In Development and Learning and Epirobotics (ICDL-Epirob), IEEE International Conference on.
Strub, C., Wörgötter, F., Ritter, H., & Sandamirskaya, Y.. (2014). Using Haptics to Extract Object Shape from Rotational Manipulations. In Intelligent Robots and Systems (IROS), IEEE/RSJ International Conference on. IEEE.
Kazerounian, S., Luciw, M., Richter, M., & Sandamirskaya, Y.. (2013). Autonomous Reinforcement of Behavioral Sequences in Neural Dynamics. In International Joint Conference on Neural Networks (IJCNN).
Lomp, O., Zibner, S. K. U., Richter, M., Ranó, I., & Schöner, G.. (2013). A software framework for cognition, embodiment, dynamics, and autonomy in robotics: cedar. In Artificial Neural Networks and Machine Learning–ICANN 2013 (pp. 475–482). Springer.
Luciw, M., Kazerounian, S., Lakhmann, K., Richter, M., & Sandamirskaya, Y.. (2013). Learning the Perceptual Conditions of Satisfaction of Elementary Behaviors. In Robotics: Science and Systems (RSS), Workshop "Active Learning in Robotics: Exploration, Curiosity, and Interaction".
Sandamirskaya, Y. (2013). Dynamic Neural Fields as a Step Towards Cognitive Neuromorphic Architectures. Frontiers in Neuroscience, 7, 276.
Sandamirskaya, Y., & Conradt, J. (2013). Increasing Autonomy of Learning SensorimotorTransformations with Dynamic Neural Fields. In International Conference on Robotics and Automation (ICRA), Workshop "Autonomous Learning".
Sandamirskaya, Y., & Conradt, J. (2013). Learning Sensorimotor Transformations with Dynamic Neural Fields. In International Conference on Artificial Neural Networks (ICANN).
Sandamirskaya, Y., Zibner, S. K. U., Schneegans, S., & Schöner, G.. (2013). Using Dynamic Field Theory to Extend the Embodiment Stance toward Higher Cognition. New Ideas in Psychology, 31(3), 322–339.
Grimme, B., Lipinski, J., & Schöner, G.. (2012). Naturalistic arm movements during obstacle avoidance in 3D and the identification of movement primitives. Experimental brain research, 222(3), 185–200. http://doi.org/10.1007/s00221-012-3205-6
Duran,, & Sandamirskaya, Y.. (2012). Neural Dynamics of Hierarchically Organized Sequences: a Robotic Implementation. In Proceedings of 2012 IEEE-RAS International Conference on Humanoid Robots (Humanoids).
Duran, B., Sandamirskaya, Y., & Schöner, G.. (2012). A Dynamic Field Architecture for the Generation of Hierarchically Organized Sequences. 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. http://doi.org/10.1007/978-3-642-33269-2_4
Klaes, C., Schneegans, S., Schöner, G., & Gail, A. (2012). Sensorimotor Learning Biases Choice Behavior: A Learning Neural Field Model for Decision Making. PLoS computational biology, 8(11), e1002774.
Lins, J., Schneegans, S., Spencer, J., & Schöner, G.. (2012). The Function and Fallibility of Visual Feature Integration: A Dynamic Neural Field Model of Illusory Conjunctions. Frontiers in Computational Neuroscience, (128). http://doi.org/10.3389/conf.fncom.2012.55.00128
Lipinski, J., Schneegans, S., Sandamirskaya, Y., Spencer, J. P., & Schöner, G.. (2012). A Neuro-Behavioral Model of Flexible Spatial Language Behaviors. Journal of Experimental Psychology: Learning, Memory and Cognition., 38(6), 1490–1511.
Park, E., Schöner, G., & Scholz, J. P. (2012). Functional synergies underlying control of upright posture during changes in head orientation. PLoS ONE, 7(8), 1–12. http://doi.org/10.1371/journal.pone.0041583
Richter, M., Sandamirskaya, Y., & Schöner, G.. (2012). A robotic architecture for action selection and behavioral organization inspired by human cognition. In IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS.
Schneegans, S., & Schöner, G.. (2012). A neural mechanism for coordinate transformation predicts pre-saccadic remapping. Biological cybernetics, 106(2), 89–109.
Scholz, J. P., Park, E., Jeka, J. J., Schöner, G., & Kiemel, T. (2012). How visual information links to multijoint coordination during quiet standing. Experimental Brain Research, 222, 229–239. http://doi.org/10.1007/s00221-012-3210-9
van Hengel, U., Sandamirskaya, Y., Schneegans, S., & Schöner, G.. (2012). A neural-dynamic architecture for flexible spatial language: intrinsic frames, the term “between”, and autonomy. In 21st IEEE International Symposium on Robot and Human Interactive Communication (Ro-Man) 2012 (pp. 150–157).
Hock, H. S., Schöner, G., Brownlow, S., & Taler, D. (2011). The temporal dynamics of global-to-local feedback in the formation of hierarchical motion patterns: psychophysics and computational simulations. Attention, perception & psychophysics, 73(4), 1171–94. http://doi.org/10.3758/s13414-011-0105-y
Grimme, B., Fuchs, S., Perrier, P., & Schöner, G.. (2011). Limb versus speech motor control: a conceptual review. Motor control, 15(1), 5–33. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/21339512
Reimann, H., Iossifidis, I., & Schöner, G.. (2011). Autonomous movement generation for manipulators with multiple simultaneous constraints using the attractor dynamics approach. In 2011 IEEE International Conference on Robotics and Automation, ICRA2011.
Sandamirskaya, Y., Richter, M., & Schöner, G.. (2011). A neural-dynamic architecture for behavioral organization of an embodied agent. In IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL EPIROB 2011) (pp. 1–7).
Scholz, J. P., Dwight-Higgin, T., Lynch, J. E., Tseng, Y. -W., Martin, V., & Schöner, G.. (2011). Motor equivalence and self-motion induced by different movement speeds. Experimental Brain Research, 209(3), 319–332. http://doi.org/10.1007/s00221-011-2541-2
Zibner, S. K. U., Faubel, C., Iossifidis, I., & Schöner, G.. (2011). Dynamic Neural Fields as Building Blocks of a Cortex-Inspired Architecture for Robotic Scene Representation. IEEE Transactions on Autonomous Mental Development, 3(1), 74–91.
Zibner, S. K. U., Faubel, C., & Schöner, G.. (2011). Making a robotic scene representation accessible to feature and label queries. In Proceedings of the First Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL EPIROB 2011).
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Hock, H. S., & Schöner, G.. (2010). Measuring Perceptual Hysteresis with the Modified Method of Limits: Dynamics at the Threshold. Seeing and Perceiving, 23, 173–195.
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Sandamirskaya, Y., Lipinski, J., Iossifidis, I., & Schöner, G.. (2010). Natural human-robot interaction through spatial language: a dynamic neural fields approach. In 19th IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN (pp. 600–607). Viareggio, Italy. http://doi.org/10.1109/ROMAN.2010.5598671
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Sandamirskaya, Y., & Schöner, G.. (2010). Serial order in an acting system: a multidimensional dynamic neural fields implementation. In Development and Learning, 2010. ICDL 2010. 9th IEEE International Conference on.
Zibner, S. K. U., Faubel, C., Iossifidis, I., & Schöner, G.. (2010). Scene Representation for Anthropomorphic Robots: A Dynamic Neural Field Approach. In ISR / ROBOTIK 2010. Munich, Germany.
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Johnson, J. S., Spencer, J. P., & Schöner, G.. (2009). A layered neural architecture for the consolidation, maintenance, and updating of representations in visual working memory. Brain research, 1299, 17–32. http://doi.org/10.1016/j.brainres.2009.07.008
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Lipinski, J., Sandamirskaya, Y., & Schöner, G.. (2009). Swing it to the Left, Swing it to the Right: Enacting Flexible Spatial Language Using a Neurodynamic Framework. Cognitive Neurodynamics, 3(4).
Lipinski, J., Sandamirskaya, Y., & Schöner, G.. (2009). An Integrative Framework for Spatial Language and Color: Robotic Demonstrations Using the Dynamic Field Theory. In 31th Annual Meeting of the Cognitive Science Society, CogSci 2009. Amstredam, NL.
Lipinski, J., Sandamirskaya, Y., & Schöner, G.. (2009). Behaviorally Flexible Spatial Communication: Robotic Demonstrations of a Neurodynamic Framework. In B. Mertsching, Hund, M., & Z., A. (Eds.), KI 2009, Lecture Notes in Artificial Intelligence (Vol. 5803, pp. 257–264). Berlin: Springer-Verlag.
Martin, V., Scholz, J. P., & Schöner, G.. (2009). Redundancy, self-motion and motor control. Neural Computation, 21(5), 1371–1414.
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