Physical Reservoir Computing in Robotics
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[1] Keyan Zahedi,et al. Morphological Computation: Synergy of Body and Brain , 2017, Entropy.
[2] Helmut Hauser,et al. A soft body as a reservoir: case studies in a dynamic model of octopus-inspired soft robotic arm , 2013, Front. Comput. Neurosci..
[3] Harald Haas,et al. Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication , 2004, Science.
[4] Tao Li,et al. Exploiting the Dynamics of Soft Materials for Machine Learning , 2018, Soft robotics.
[5] Helmut Hauser,et al. Mass-Spring Damper Array as a Mechanical Medium for Computation , 2018, ICANN.
[6] Andrew Philippides,et al. Active Shape Discrimination with Physical Reservoir Computers , 2014, ALIFE.
[7] Rodolphe Sepulchre,et al. Differential Dissipativity Theory for Dominance Analysis , 2017, IEEE Transactions on Automatic Control.
[8] Helmut Hauser,et al. Morphological computation-based control of a modular, pneumatically driven, soft robotic arm , 2018, Adv. Robotics.
[9] Helmut Hauser,et al. Resilient Intelligent Machines through Morphological Changes , 2019 .
[10] Keyan Zahedi,et al. Mathematik in den Naturwissenschaften Leipzig Quantifying Morphological Computation based on an Information Decomposition of the Sensorimotor Loop , 2015 .
[11] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[12] Helmut Hauser,et al. Exploiting short-term memory in soft body dynamics as a computational resource , 2014, Journal of The Royal Society Interface.
[13] Helmut Hauser,et al. Moving a robot arm by exploiting its complex compliant morphology , 2011 .
[14] Helmut Hauser. Morphological computation - A potential solution for the control problem in soft robotics , 2016 .
[15] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[16] Helmut Hauser,et al. Self-exploration of the Stumpy Robot with Predictive Information Maximization , 2014, SAB.
[17] Benjamin Schrauwen,et al. Design and control of compliant tensegrity robots through simulation and hardware validation , 2014, Journal of The Royal Society Interface.
[18] Helmut Hauser,et al. Spine dynamics as a computational resource in spine-driven quadruped locomotion , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[19] L. F. Abbott,et al. Generating Coherent Patterns of Activity from Chaotic Neural Networks , 2009, Neuron.
[20] Helmut Hauser,et al. Sensing Through the Body - Non-Contact Object Localisation Using Morphological Computation , 2019, 2019 2nd IEEE International Conference on Soft Robotics (RoboSoft).
[21] R. Bernhardsgrütter,et al. Employing L-systems to generate mass-spring networks for morphological computing , 2014 .
[22] Amir F. Atiya,et al. New results on recurrent network training: unifying the algorithms and accelerating convergence , 2000, IEEE Trans. Neural Networks Learn. Syst..
[23] Ralf Der,et al. Information Driven Self-Organization of Complex Robotic Behaviors , 2013, PloS one.
[24] Jonathan Rossiter,et al. Bodily Aware Soft Robots: Integration of Proprioceptive and Exteroceptive Sensors , 2017, ICRA.
[25] Helmut Hauser,et al. The role of feedback in morphological computation with compliant bodies , 2012, Biological Cybernetics.
[26] Phil Husbands,et al. Feathered Flyer: Integrating Morphological Computation and Sensory Reflexes into a Physically Simulated Flapping-Wing Robot for Robust Flight Manoeuvre , 2007, ECAL.
[27] Kohei Nakajima,et al. Morphological Computation: The Body as a Computational Resource , 2014 .
[28] Benjamin Schrauwen,et al. The Introduction of Time-Scales in Reservoir Computing, Applied to Isolated Digits Recognition , 2007, ICANN.
[29] Leon O. Chua,et al. Fading memory and the problem of approximating nonlinear operators with volterra series , 1985 .
[30] Rolf H. Luchsinger,et al. Morphological computation : applications on different scales exploiting classical and statistical mechanics , 2011 .
[31] Helmut Hauser,et al. Computation with mechanically coupled springs for compliant robots , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[32] Keisuke Fujii,et al. Harnessing disordered quantum dynamics for machine learning , 2016, 1602.08159.
[33] Jean-Jacques E. Slotine,et al. On Contraction Analysis for Non-linear Systems , 1998, Autom..
[34] Barry A. Trimmer,et al. A constitutive model for active–passive transition of muscle fibers , 2012 .
[35] Chrisantha Fernando,et al. Pattern Recognition in a Bucket , 2003, ECAL.
[36] Helmut Hauser,et al. Towards a theoretical foundation for morphological computation with compliant bodies , 2011, Biological Cybernetics.
[37] Jean-Jacques E. Slotine,et al. Modularity, evolution, and the binding problem: a view from stability theory , 2001, Neural Networks.
[38] Benjamin Schrauwen,et al. Automated Design of Complex Dynamic Systems , 2014, PloS one.
[39] Jeremy L. England,et al. Self-Organized Resonance during Search of a Diverse Chemical Space. , 2017, Physical review letters.
[40] Claudine Chaouiya,et al. Bringing Dicynodonts Back to Life: Paleobiology and Anatomy of a New Emydopoid Genus from the Upper Permian of Mozambique , 2013, PloS one.
[41] Eduardo D. Sontag,et al. Computational Aspects of Feedback in Neural Circuits , 2006, PLoS Comput. Biol..
[42] Benjamin Schrauwen,et al. Locomotion Without a Brain: Physical Reservoir Computing in Tensegrity Structures , 2013, Artificial Life.
[43] Benjamin Schrauwen,et al. Analog readout for optical reservoir computers , 2012, NIPS.
[44] Helmut Hauser,et al. Morphological Computation and Morphological Control: Steps Toward a Formal Theory and Applications , 2013, Artificial Life.
[45] Henry Markram,et al. Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations , 2002, Neural Computation.
[46] Kurt Hornik,et al. Multilayer feedforward networks are universal approximators , 1989, Neural Networks.
[47] Wolfgang Maass,et al. Emergence of complex computational structures from chaotic neural networks through reward-modulated Hebbian learning. , 2014, Cerebral cortex.
[48] Joni Dambre,et al. Trainable hardware for dynamical computing using error backpropagation through physical media , 2014, Nature Communications.
[49] Helmut Hauser,et al. Morphosis—Taking Morphological Computation to the Next Level , 2017 .