Quantifying the Evolutionary Self Structuring of Embodied Cognitive Networks

We outline a possible theoretical framework for the quantitative modeling of networked embodied cognitive systems. We notice that: 1) information self structuring through sensory-motor coordination does not deterministically occur in Rn vector space, a generic multivariable space, but in SE(3), the group structure of the possible motions of a body in space; 2) it happens in a stochastic open ended environment. These observations may simplify, at the price of a certain abstraction, the modeling and the design of self organization processes based on the maximization of some informational measures, such as mutual information. Furthermore, by providing closed form or computationally lighter algorithms, it may significantly reduce the computational burden of their implementation. We propose a modeling framework which aims to give new tools for the design of networks of new artificial self organizing, embodied and intelligent agents and the reverse engineering of natural ones. At this point, it represents much a theoretical conjecture and it has still to be experimentally verified whether this model will be useful in practice.

Reachable Workspace and Proximal Function Measures for Quantifying Upper Limb Motion.

There are a lack of quantitative measures for clinically assessing upper limb function. Conventional biomechanical performance measures are restricted to specialist labs due to hardware cost and complexity, while the resulting measurements require specialists for analysis. Depth cameras are low cost and portable systems that can track surrogate joint positions. However, these motions may not be biologically consistent, which can result in noisy, inaccurate movements. This paper introduces a rigid body modelling method to enforce biological feasibility of the recovered motions. This method is evaluated on an existing depth camera assessment: the reachable workspace (RW) measure for assessing gross shoulder function. As a rigid body model is used, position estimates of new proximal targets can be added, resulting in a proximal function (PF) measure for assessing a subject's ability to touch specific body landmarks. The accuracy, and repeatability of these measures is assessed on ten asymptomatic subjects, with and without rigid body constraints. This analysis is performed both on a low-cost depth camera system and a gold-standard active motion capture system. The addition of rigid body constraints was found to improve accuracy and concordance of the depth camera system, particularly in lateral reaching movements. Both RW and PF measures were found to be feasible candidates for clinical assessment, with future analysis needed to determine their ability to detect changes within specific patient populations

Efficient collective swimming by harnessing vortices through deep reinforcement learning

Fish in schooling formations navigate complex flow-fields replete with mechanical energy in the vortex wakes of their companions. Their schooling behaviour has been associated with evolutionary advantages including collective energy savings. How fish harvest energy from their complex fluid environment and the underlying physical mechanisms governing energy-extraction during collective swimming, is still unknown. Here we show that fish can improve their sustained propulsive efficiency by actively following, and judiciously intercepting, vortices in the wake of other swimmers. This swimming strategy leads to collective energy-savings and is revealed through the first ever combination of deep reinforcement learning with high-fidelity flow simulations. We find that a `smart-swimmer' can adapt its position and body deformation to synchronise with the momentum of the oncoming vortices, improving its average swimming-efficiency at no cost to the leader. The results show that fish may harvest energy deposited in vortices produced by their peers, and support the conjecture that swimming in formation is energetically advantageous. Moreover, this study demonstrates that deep reinforcement learning can produce navigation algorithms for complex flow-fields, with promising implications for energy savings in autonomous robotic swarms.Comment: 26 pages, 14 figure

Inertial amplification of continuous structures: Large band gaps from small masses

Wave motion in a continuous elastic rod with a periodically attached inertial-amplification mechanism is investigated. The mechanism has properties similar to an "inerter" typically used in vehicle suspensions, however here it is constructed and utilized in a manner that alters the intrinsic properties of a continuous structure. The elastodynamic band structure of the hybrid rod-mechanism structure yields band gaps that are exceedingly wide and deep when compared to what can be obtained using standard local resonators, while still being low in frequency. With this concept, a large band gap may be realized with as much as twenty times less added mass compared to what is needed in a standard local resonator configuration. The emerging inertially enhanced continuous structure also exhibits unique qualitative features in its dispersion curves. These include the existence of a characteristic double-peak in the attenuation constant profile within gaps and the possibility of coalescence of two neighbouring gaps creating a large contiguous gap.Comment: Manuscript is under review for journal publicatio

The Rigidity of Spherical Frameworks: Swapping Blocks and Holes

A significant range of geometric structures whose rigidity is explored for both practical and theoretical purposes are formed by modifying generically isostatic triangulated spheres. In the block and hole structures (P, p), some edges are removed to make holes, and other edges are added to create rigid sub-structures called blocks. Previous work noted a combinatorial analogy in which blocks and holes played equivalent roles. In this paper, we connect stresses in such a structure (P, p) to first-order motions in a swapped structure (P', p), where holes become blocks and blocks become holes. When the initial structure is geometrically isostatic, this shows that the swapped structure is also geometrically isostatic, giving the strongest possible correspondence. We use a projective geometric presentation of the statics and the motions, to make the key underlying correspondences transparent.Comment: 36 pages, 9 figure