The multi-functional foot in athletic movement: Extraordinary feats by our extraordinary feet

The unique architecture of the foot system provides a sensitive, multi-tensional method of communicating with the surrounding environment. Within the premise of the paper, we discuss three themes: complexity, degeneracy and bio-tensegrity. Complex structures within the foot allow the human movement system to negotiate strategies for dynamic movement during athletic endeavours. We discuss such complex structures with particular attention to properties of a bio-tensegrity system. Degeneracy within the foot structure offers a distinctive solution to the problems posed by differing terrains and uneven surfaces allowing lower extremity structures to overcome perturbation as and when it occurs. This extraordinary structure offers a significant contribution to bipedalism through presenting a robust base of support and as such, should be given more consideration when designing athletic development programmes

Uniqueness of human running coordination: The integration of modern and ancient evolutionary innovations

Running is a pervasive activity across human cultures and a cornerstone of contemporary health, fitness and sporting activities. Yet for the overwhelming predominance of human existence running was an essential prerequisite for survival. A means to hunt, and a means to escape when hunted. In a very real sense humans have evolved to run. Yet curiously, perhaps due to running’s cultural ubiquity and the natural ease with which we learn to run, we rarely consider the uniqueness of human bipedal running within the animal kingdom. Our unique upright, single stance, bouncing running gait imposes a unique set of coordinative difficulties. Challenges demanding we precariously balance our fragile brains in the very position where they are most vulnerable to falling injury while simultaneously retaining stability, steering direction of travel, and powering the upcoming stride: all within the abbreviated time-frames afforded by short, violent ground contacts separated by long flight times. These running coordination challenges are solved through the tightly-integrated blending of primitive evolutionary legacies, conserved from reptilian and vertebrate lineages, and comparatively modern, more exclusively human, innovations. The integrated unification of these top-down and bottom-up control processes bestows humans with an agile control system, enabling us to readily modulate speeds, change direction, negotiate varied terrains and to instantaneously adapt to changing surface conditions. The seamless integration of these evolutionary processes is facilitated by pervasive, neural and biological, activity-dependent adaptive plasticity. Over time, and with progressive exposure, this adaptive plasticity shapes neural and biological structures to best cope with regularly imposed movement challenges. This pervasive plasticity enables the gradual construction of a robust system of distributed coordinated control, comprised of processes that are so deeply collectively entwined that describing their functionality in isolation obscures their true irrevocably entangled nature. Although other species rely on a similar set of coordinated processes to run, the bouncing bipedal nature of human running presents a specific set of coordination challenges, solved using a customized blend of evolved solutions. A deeper appreciation of the foundations of the running coordination phenomenon promotes conceptual clarity, potentially informing future advances in running training and running-injury rehabilitation interventions

On Neuromechanical Approaches for the Study of Biological Grasp and Manipulation

Biological and robotic grasp and manipulation are undeniably similar at the level of mechanical task performance. However, their underlying fundamental biological vs. engineering mechanisms are, by definition, dramatically different and can even be antithetical. Even our approach to each is diametrically opposite: inductive science for the study of biological systems vs. engineering synthesis for the design and construction of robotic systems. The past 20 years have seen several conceptual advances in both fields and the quest to unify them. Chief among them is the reluctant recognition that their underlying fundamental mechanisms may actually share limited common ground, while exhibiting many fundamental differences. This recognition is particularly liberating because it allows us to resolve and move beyond multiple paradoxes and contradictions that arose from the initial reasonable assumption of a large common ground. Here, we begin by introducing the perspective of neuromechanics, which emphasizes that real-world behavior emerges from the intimate interactions among the physical structure of the system, the mechanical requirements of a task, the feasible neural control actions to produce it, and the ability of the neuromuscular system to adapt through interactions with the environment. This allows us to articulate a succinct overview of a few salient conceptual paradoxes and contradictions regarding under-determined vs. over-determined mechanics, under- vs. over-actuated control, prescribed vs. emergent function, learning vs. implementation vs. adaptation, prescriptive vs. descriptive synergies, and optimal vs. habitual performance. We conclude by presenting open questions and suggesting directions for future research. We hope this frank assessment of the state-of-the-art will encourage and guide these communities to continue to interact and make progress in these important areas

Technical report on hierarchical reservoir computing architectures

One approach for building architectures (of which an overview was given in D.6.1) in AMARSi is to use reservoir computing. Here, untrained (or unsupervised trained) recurrent neural networks are used for motion control by learning simple readouts on the dynamic representation generated by the dynamic RNN system. Although single reservoirs are able to generate rich and tunable control patterns (as demonstrated in D.4.1), to allow composition of motion or high-level control, these modules need to be built in an architecture. An active research area in reservoir computing is to build hierarchical reservoir systems. The main reason for this is that reservoirs basically are band-pass systems and can only represent information in a limited frequency band. If information at both fast and slow timescales needs to be integrated, a natural approach is to build a hierarchical system where each layer operates at a different time scale. The big challenge in these hierarchies is how to learn intermediate representations that link the various layers, and especially how bottom-up and top-down information flows need to be organized. We believe that these hierarchical reservoir computing systems are good candidates to build (at least part of) architectures required in AMARSi for rich motor control. In this short deliverable we give an overview of and references to current approaches in hierarchical reservoir computing, several of which have been investigated on speech and handwriting recognition problems in the sister EU project ORGANIC (http://reservoir- computing.org/organic). Many of these hierarchical systems can be used to not only generate dynamical feature hierarchies, but are also able to learn a hierarchy of pattern controller, of special interest to the AMARSi project