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

On neuromechanical approaches for the study of biological and robotic grasp and manipulation

abstract: 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 and open-minded assessment of the state-of-the-art will encourage and guide these communities to continue to interact and make progress in these important areas at the interface of neuromechanics, neuroscience, rehabilitation and robotics.The electronic version of this article is the complete one and can be found online at: https://jneuroengrehab.biomedcentral.com/articles/10.1186/s12984-017-0305-

Cortical Models for Movement Control

Defense Advanced Research Projects Agency and Office of Naval Research (N0014-95-l-0409)

Evolvability signatures of generative encodings: beyond standard performance benchmarks

Evolutionary robotics is a promising approach to autonomously synthesize machines with abilities that resemble those of animals, but the field suffers from a lack of strong foundations. In particular, evolutionary systems are currently assessed solely by the fitness score their evolved artifacts can achieve for a specific task, whereas such fitness-based comparisons provide limited insights about how the same system would evaluate on different tasks, and its adaptive capabilities to respond to changes in fitness (e.g., from damages to the machine, or in new situations). To counter these limitations, we introduce the concept of "evolvability signatures", which picture the post-mutation statistical distribution of both behavior diversity (how different are the robot behaviors after a mutation?) and fitness values (how different is the fitness after a mutation?). We tested the relevance of this concept by evolving controllers for hexapod robot locomotion using five different genotype-to-phenotype mappings (direct encoding, generative encoding of open-loop and closed-loop central pattern generators, generative encoding of neural networks, and single-unit pattern generators (SUPG)). We observed a predictive relationship between the evolvability signature of each encoding and the number of generations required by hexapods to adapt from incurred damages. Our study also reveals that, across the five investigated encodings, the SUPG scheme achieved the best evolvability signature, and was always foremost in recovering an effective gait following robot damages. Overall, our evolvability signatures neatly complement existing task-performance benchmarks, and pave the way for stronger foundations for research in evolutionary robotics.Comment: 24 pages with 12 figures in the main text, and 4 supplementary figures. Accepted at Information Sciences journal (in press). Supplemental videos are available online at, see http://goo.gl/uyY1R

Active Vision Strategies in Predation

Visual predation requires precise and accurate behaviour, for which many predators have evolved excellent visual skills. However, an animal's visual abilities are greatly affected by how it moves its eyes, known as active vision. Insects have immobile eyes but can direct their gaze by moving their heads and bodies. This thesis examines three predatory insects with different predatory strategies, to understand the extent to which active vision can be used in predation. The first experimental chapter considers the African praying mantid, Sphodromantis lineola. Praying mantids are stationary terrestrial predators, which use their extremely mobile necks to visually track prey until it is within reach. By using statistical models, we identified what factors elicited strikes and, importantly, their success rate. The timing of head movements greatly increased the chances of strike success, with earlier movements increasing the success rate. The second experimental chapter addresses how darting robber flies, Psilonyx annulatus, aerially attack static prey. Prior to attacking, darting robber flies translate their body around a central point, assessing their prey. After assessment, they attack from a position correlated with the target's absolute size, not its angular size. Prey is beyond the robber fly's stereopsis range during the period of assessment. Assessments of differently sized targets have similarities with the behaviour exhibited by jumping insects, which use motion parallax, a form of active vision, to assess jump distance, suggesting darting robber flies also use motion parallax to predate. The final experimental chapter considers killer flies, Coenosia attenuata, which chase moving targets aerially. Killer flies use a combination of gravity and wing acceleration to increase their speed when chasing prey from above. This increased speed restricts the flies' ability to steer. However, killer flies create strong looming stimuli which may trigger their prey to produce evasive manoeuvres, thereby slowing down. Moreover, by travelling faster towards their prey, killer flies may avoid losing track of it, a real danger when chasing moving prey with low- resolution eyes potentially avoided thanks to active vision. By employing active vision, each of the predators considered can achieve impressive performances, despite relying on very different strategies to capture prey. The use of active vision can increase the success of already excellent visual predators and improve the performance of predator with limited vision. However, active vision can also substantially alter predatory behaviour, leading to a trade- off between the advantages in visual perception active vision can bring and the disadvantage in behavioural efficiency of active vision strategies

Embodied cognition: A field guide

The nature of cognition is being re-considered. Instead of emphasizing formal operations on abstract symbols, the new approach foregrounds the fact that cognition is, rather, a situated activity, and suggests that thinking beings ought therefore be considered first and foremost as acting beings. The essay reviews recent work in Embodied Cognition, provides a concise guide to its principles, attitudes and goals, and identifies the physical grounding project as its central research focus

From skin to brain:modelling a whole-body coordination scenario of nervous system origin

Nervous systems are ubiquitous in the animal kingdom, yet the evolutionary origin of this essential feature, the basis of human cognition, is unclear. Since the emergence of nervous systems happened at least 430 but likely as much as 600 million years ago, there is little hard evidence to illustrate this evolutionary process. To understand the evolutionary origin of nervous systems, theoretical frameworks putting what evidence there is into context are crucial.One such framework, the interal coordination view, posits that nervous systems arose in order to allow early animals to coordinate their multicellular bodies as a whole. In this research, we explored potential intermediate evolutionary steps on the road to a true nervous system. To that end, we used computational models of very simple simulated animals. In these simulations, we investigated mechanisms short of nervous systems, using (simulated) biological building blocks which would likely have been present in animals at the time nervous systems evolved.These models demonstrate that even very rudimentary mechanisms have the potential of providing useful coordination to early animals, thereby supporting the internal coordination view of nervous system origin: nervous systems likely evolved to allow whole-body coordination

Opinions and Outlooks on Morphological Computation

Morphological Computation is based on the observation that biological systems seem to carry out relevant computations with their morphology (physical body) in order to successfully interact with their environments. This can be observed in a whole range of systems and at many different scales. It has been studied in animals – e.g., while running, the functionality of coping with impact and slight unevenness in the ground is "delivered" by the shape of the legs and the damped elasticity of the muscle-tendon system – and plants, but it has also been observed at the cellular and even at the molecular level – as seen, for example, in spontaneous self-assembly. The concept of morphological computation has served as an inspirational resource to build bio-inspired robots, design novel approaches for support systems in health care, implement computation with natural systems, but also in art and architecture. As a consequence, the field is highly interdisciplinary, which is also nicely reflected in the wide range of authors that are featured in this e-book. We have contributions from robotics, mechanical engineering, health, architecture, biology, philosophy, and others