A soft body as a reservoir: case studies in a dynamic model of octopus-inspired soft robotic arm

The behaviors of the animals or embodied agents are characterized by the dynamic coupling between the brain, the body, and the environment. This implies that control, which is conventionally thought to be handled by the brain or a controller, can partially be outsourced to the physical body and the interaction with the environment. This idea has been demonstrated in a number of recently constructed robots, in particular from the field of “soft robotics”. Soft robots are made of a soft material introducing high-dimensionality, non-linearity, and elasticity, which often makes the robots difficult to control. Biological systems such as the octopus are mastering their complex bodies in highly sophisticated manners by capitalizing on their body dynamics. We will demonstrate that the structure of the octopus arm cannot only be exploited for generating behavior but also, in a sense, as a computational resource. By using a soft robotic arm inspired by the octopus we show in a number of experiments how control is partially incorporated into the physical arm's dynamics and how the arm's dynamics can be exploited to approximate non-linear dynamical systems and embed non-linear limit cycles. Future application scenarios as well as the implications of the results for the octopus biology are also discussed.

[1]  W. Kier,et al.  Tongues, tentacles and trunks: the biomechanics of movement in muscular‐hydrostats , 1985 .

[2]  M. Alexander,et al.  Principles of Neural Science , 1981 .

[3]  W. Kier,et al.  Trunks, Tongues, and Tentacles: Moving with Skeletons of Muscle , 1989 .

[4]  N. Franceschini,et al.  From insect vision to robot vision , 1992 .

[5]  Y Gutfreund,et al.  Organization of Octopus Arm Movements: A Model System for Studying the Control of Flexible Arms , 1996, The Journal of Neuroscience.

[6]  B. Hochner,et al.  Patterns of Arm Muscle Activation Involved in Octopus Reaching Movements , 1998, The Journal of Neuroscience.

[7]  Amir F. Atiya,et al.  New results on recurrent network training: unifying the algorithms and accelerating convergence , 2000, IEEE Trans. Neural Networks Learn. Syst..

[8]  B. Hochner,et al.  Control of Octopus Arm Extension by a Peripheral Motor Program , 2001, Science.

[9]  Auke Jan Ijspeert,et al.  A connectionist central pattern generator for the aquatic and terrestrial gaits of a simulated salamander , 2001, Biological Cybernetics.

[10]  Herbert Jaeger,et al.  Adaptive Nonlinear System Identification with Echo State Networks , 2002, NIPS.

[11]  R. Lieber Skeletal Muscle Structure, Function, and Plasticity: The Physiological Basis of Rehabilitation , 2002 .

[12]  W. Kier,et al.  Fast muscle in squid (Loligo pealei): contractile properties of a specialized muscle fibre type. , 2002, The Journal of experimental biology.

[13]  Henry Markram,et al.  Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations , 2002, Neural Computation.

[14]  Akihiko Saito Skeletal Muscle Structure. , 2003 .

[15]  W. Kier,et al.  Switching Skeletons: Hydrostatic Support in Molting Crabs , 2003, Science.

[16]  Harald Haas,et al.  Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication , 2004, Science.

[17]  Tamar Flash,et al.  Dynamic model of the octopus arm. I. Biomechanics of the octopus reaching movement. , 2005, Journal of neurophysiology.

[18]  Tamar Flash,et al.  Dynamic model of the octopus arm. II. Control of reaching movements. , 2005, Journal of neurophysiology.

[19]  Herbert Jaeger,et al.  A tutorial on training recurrent neural networks , covering BPPT , RTRL , EKF and the " echo state network " approach - Semantic Scholar , 2005 .

[20]  Russ Tedrake,et al.  Efficient Bipedal Robots Based on Passive-Dynamic Walkers , 2005, Science.

[21]  Germán Sumbre,et al.  Neurobiology: Motor control of flexible octopus arms , 2005, Nature.

[22]  Rolf Pfeifer,et al.  How the body shapes the way we think - a new view on intelligence , 2006 .

[23]  Michael Günther,et al.  Intelligence by mechanics , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[24]  Herbert Jaeger,et al.  Echo state network , 2007, Scholarpedia.

[25]  R. Pfeifer,et al.  Self-Organization, Embodiment, and Biologically Inspired Robotics , 2007, Science.

[26]  Benjamin Schrauwen,et al.  An experimental unification of reservoir computing methods , 2007, Neural Networks.

[27]  C. Laschi,et al.  Biorobotic Investigation on the Muscle Structure of an Octopus Tentacle , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[28]  Benjamin Schrauwen,et al.  An overview of reservoir computing: theory, applications and implementations , 2007, ESANN.

[29]  Ludovic Righetti,et al.  Pattern generators with sensory feedback for the control of quadruped locomotion , 2008, 2008 IEEE International Conference on Robotics and Automation.

[30]  Ian D. Walker,et al.  Soft robotics: Biological inspiration, state of the art, and future research , 2008 .

[31]  Heinrich M. Jaeger,et al.  JSEL: Jamming Skin Enabled Locomotion , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[32]  B Mazzolai,et al.  Design of a biomimetic robotic octopus arm , 2009, Bioinspiration & biomimetics.

[33]  Herbert Jaeger,et al.  Reservoir computing approaches to recurrent neural network training , 2009, Comput. Sci. Rev..

[34]  P. Holmes,et al.  Reflexes and preflexes: on the role of sensory feedback on rhythmic patterns in insect locomotion , 2010, Biological Cybernetics.

[35]  M. Fink,et al.  Real‐time visualization of muscle stiffness distribution with ultrasound shear wave imaging during muscle contraction , 2010, Muscle & nerve.

[36]  Heinrich M. Jaeger,et al.  Universal robotic gripper based on the jamming of granular material , 2010, Proceedings of the National Academy of Sciences.

[37]  Benjamin Schrauwen,et al.  The body as a reservoir: locomotion and sensing with linear feedback , 2011 .

[38]  Dimitris P. Tsakiris,et al.  Dynamic model of a hyper-redundant, octopus-like manipulator for underwater applications , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[39]  Helmut Hauser,et al.  Computation with mechanically coupled springs for compliant robots , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[40]  Matteo Cianchetti,et al.  Finding structure in deadtime , 2011 .

[41]  Filip Ilievski,et al.  Multigait soft robot , 2011, Proceedings of the National Academy of Sciences.

[42]  Tao Li,et al.  Information theoretic analysis on a soft robotic arm inspired by the octopus , 2011, 2011 IEEE International Conference on Robotics and Biomimetics.

[43]  Tao Li,et al.  Harnessing the dynamics of a soft body with “timing”: Octopus inspired control via recurrent neural networks , 2011, 2011 15th International Conference on Advanced Robotics (ICAR).

[44]  Darwin G. Caldwell,et al.  Dynamic continuum arm model for use with underwater robotic manipulators inspired by octopus vulgaris , 2012, 2012 IEEE International Conference on Robotics and Automation.

[45]  B. Hochner FUNCTIONAL MORPHOLOGY OF THE NEUROMUSCULAR SYSTEM OF THE oCtoPuS VulGariS ARM , 2012 .

[46]  C. Laschi,et al.  Octopus-inspired sensorimotor control of a multi-arm soft robot , 2012, 2012 IEEE International Conference on Mechatronics and Automation.

[47]  J. A. Ekaterinaris,et al.  Generation of primitive behaviors for non-linear hyperelastic octopus-inspired robotic arm , 2012, 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob).

[48]  Kohei Nakajima,et al.  FROM THE OCTOPUS TO SOFT ROBOTS CONTROL: AN OCTOPUS INSPIRED BEHAVIOR CONTROL ARCHITECTURE FOR SOFT ROBOTS , 2012 .

[49]  Tao Li,et al.  Local information transfer in soft robotic arm , 2012, 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[50]  Fumiya Iida,et al.  The challenges ahead for bio-inspired 'soft' robotics , 2012, CACM.

[51]  Darwin G. Caldwell,et al.  Dynamic modeling and control of an octopus inspired multiple continuum arm robot , 2012, Comput. Math. Appl..

[52]  Dimitris P. Tsakiris,et al.  Hydrodynamic analysis of octopus-like robotic arms , 2012, 2012 IEEE International Conference on Robotics and Automation.

[53]  Helmut Hauser,et al.  The role of feedback in morphological computation with compliant bodies , 2012, Biological Cybernetics.

[54]  Darwin G. Caldwell,et al.  Timing-based control via echo state network for soft robotic arm , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

[55]  Aubery Marchel Tientcheu Ngouabeu,et al.  Morphology-Induced Collective Behaviors: Dynamic Pattern Formation in Water-Floating Elements , 2012, PloS one.

[56]  Helmut Hauser,et al.  Towards a theoretical foundation for morphological computation with compliant bodies , 2011, Biological Cybernetics.

[57]  Paolo Dario,et al.  Soft Robot Arm Inspired by the Octopus , 2012, Adv. Robotics.

[58]  Helmut Hauser,et al.  Computing with a muscular-hydrostat system , 2013, 2013 IEEE International Conference on Robotics and Automation.

[59]  Tao Li,et al.  Online learning for behavior switching in a soft robotic arm , 2013, 2013 IEEE International Conference on Robotics and Automation.

[60]  P. Olver Nonlinear Systems , 2013 .

[61]  Benjamin Schrauwen,et al.  Locomotion Without a Brain: Physical Reservoir Computing in Tensegrity Structures , 2013, Artificial Life.

[62]  Helmut Hauser,et al.  Morphological Computation and Morphological Control: Steps Toward a Formal Theory and Applications , 2013, Artificial Life.

[63]  V Vavourakis,et al.  A nonlinear dynamic finite element approach for simulating muscular hydrostats , 2014, Computer methods in biomechanics and biomedical engineering.