Autonomous Learning

Haptic Learning

Haptic learning based on tactile feedback in the sensori-motor loop with Dynamic Neural Fields.

Learning to look

In this project, a neural-dynamic model is developed, which demonstrates how sensorimotor transformations, involved in generation of precise saccadic eye movements may be learned autonomously.

Learning to reach

In this project, the gaze-based representation, generated in the system for saccadic eye movements, is used to learn to direct the end-effector of a robotic arm towards the visually perceived target.

Neural-Dynamic Reinforcement Learning

The neural-dynamics of behavioral organisation is integrated in this project with reinforcement learning mechanisms to enable RL in a behaving robotic system.

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.
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.
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.
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.
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).
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).
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.
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
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.
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.
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).
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).
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
Sandamirskaya, Y., & Schöner, G.. (2010). An embodied account of serial order: how instabilities drive sequence generation. Neural Networks, 23(10), 1164–1179. http://doi.org/DOI: 10.1016/j.neunet.2010.07.012
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.
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.