Physical Reservoir Computing in Robotics

[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 .