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. 2010 Apr 6;7(45):613-21.
doi: 10.1098/rsif.2009.0240. Epub 2009 Sep 23.

Morphological communication: exploiting coupled dynamics in a complex mechanical structure to achieve locomotion

John A Rieffel  1 , Francisco J Valero-Cuevas, Hod Lipson
Affiliations

Morphological communication: exploiting coupled dynamics in a complex mechanical structure to achieve locomotion

John A Rieffel et al. J R Soc Interface. .

Abstract

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.

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Figures

Figure 1.
Figure 1.
A complex and highly dynamically coupled 15-bar tensegrity structure. High degrees of dynamical coupling is a systemic quality of tensegrity structures.
Figure 2.
Figure 2.
A family of tensegrity towers produced by the same grammar as the tower in figure 1. As the number of iterations of the grammar increases, the tower grows from 10 bars to 20, 30, 40 and 50, repeating the same pattern of twisting bars as it grows.
Figure 3.
Figure 3.
A tensegrity robot consisting of four strut modules and 16 strings. The strut modules consist of servo motors connected by clear plastic tubes which contain batteries and wiring.
Figure 4.
Figure 4.
Snapshots of the motion of an evolved gait over 20 000 time steps.
Figure 5.
Figure 5.
Gait trajectories showing the travel of the centre of mass over the x/y coordinate frame for faster (a–c) and slower (d–f) gait speeds for three evolved gaits (each row). As the speed of the evolved gait changes, both the distance travelled and the path traversed vary significantly. Left- and right-hand figures are not on matched scales. Any increase in distance travelled (slower gaits in left hand figure) is due to the significantly longer amount of simulator time required for the structure to complete a fixed gait cycle. At this time scale, factors such as momentum play a larger role. Blue, full; green, 2 times; red, 3 times; turquoise, 4 times; purple, 5 times; yellow, 20 times.
Figure 6.
Figure 6.
Demonstration of the emergence of communication between individual module networks via dynamical coupling. In (a) suppressing the first network causes a distal network to cease activation. Enabling the first network re-enables the second. In (b) suppressing the first network causes the second network to increase its firing frequency. After enabling the first network, the distal network resumes the former frequency. The time delay observed between behaviour shifts in the distal networks corresponds to the propagation of dynamics through the system.
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