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Machine Theory

Biomimetic and Biohybrid Systems: Third International by Armin Duff, Nathan F. Lepora, Anna Mura, Tony J. Prescott,

By Armin Duff, Nathan F. Lepora, Anna Mura, Tony J. Prescott, Paul F.M.J. Verschure

This publication constitutes the complaints of the 3rd foreign convention on Biomimetic and Biohybrid structures, residing Machines 2014, held in Barcelona, Spain, in July/August 2014. The 31 complete papers and 27 prolonged abstracts integrated during this quantity have been rigorously reviewed and chosen from sixty two submissions. the subjects lined are mind dependent structures, lively sensing, gentle robotics, studying, reminiscence, keep watch over architectures, self-regulation, flow and locomotion, sensory platforms and perception.

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Extra info for Biomimetic and Biohybrid Systems: Third International Conference, Living Machines 2014, Milan, Italy, July 30 – August 1, 2014. Proceedings

Example text

We tested the learning rule in a set-up where the target signal (z(t)) is a Gaussian that has to be approximated by weighting multiple bases functions. Since the result in Eq. 8 does not depend on z(t) or x(t), we know that the ). optimal value for knoi (kopt ) is the ratio of the cost weights ( ccaction error We performed simulations with two pairs of values for cerror and cactions , and for each pair we tested convergence for the optimal value when the initial value was either above or below the optimal.

The segments are interconnected by means of hinge joints with torsion springs (modeling bending elasticity). Adjacent chains are free to move one respect to the others, with interactions among them being modeled by means of linear springs (Fig. 2). This structure is able to mimic the passive compliance using the minimum number of DOFs. No structural damping was included as it was assumed to be negligible with respect to the one provided by fluid drag forces. Only the joints belonging to the frontal chain are actuated, by means of simple sinusoidal oscillators (the same oscillation is applied at each actuated joint).

Namely, while the on-line update rule is modifying the value of the knoi , the decorrelation learning rule is converging to the set of weights that approximate the target function. In other words, not only the correct weighting of the error and the output signals but also the output signal itself is being learned. In all but one case, at some point in the simulation, the overall cost sustainedly increases for some period before finally decreasing to the minimum value. Note that the final oscillations around the optimal value are due to the sign function in the update rule.

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