Morphological communication: exploiting coupled dynamics in a complex mechanical structure to achieve locomotion.
Traditional engineering approaches strive to avoid, or actively suppress, nonlinear dynamic coupling among components. Biological systems, in contrast, are often rife with these dynamics. Could there be, in some cases, a benefit to high degrees of dynamical coupling? Here we present a distributed robotic control scheme inspired by the biological phenomenon of tensegrity-based mechanotransduction. This emergence of morphology-as-information-conduit or 'morphological communication', enabled by time-sensitive spiking neural networks, presents a new paradigm for the decentralized control of large, coupled, modular systems. These results significantly bolster, both in magnitude and in form, the idea of morphological computation in robotic control. Furthermore, they lend further credence to ideas of embodied anatomical computation in biological systems, on scales ranging from cellular structures up to the tendinous networks of the human hand.
Rieffel
John A
JA
Mechanical Engineering Department, Cornell University, Ithaca, NY 14853, USA. rieffelj@union.edu
Valero-Cuevas
Francisco J
FJ
Lipson
Hod
H
D003142:Communication; D006801:Humans; D008124:Locomotion / Q000502:physiology*; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D012371:Robotics / Q000379:methods