In silico case studies of compliant robots: AMARSI deliverable 3.3

In the deliverable 3.2 we presented how the morphological computing ap- proach can significantly facilitate the control strategy in several scenarios, e.g. quadruped locomotion, bipedal locomotion and reaching. In particular, the Kitty experimental platform is an example of the use of morphological computation to allow quadruped locomotion. In this deliverable we continue with the simulation studies on the application of the different morphological computation strategies to control a robotic system

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

Design, Actuation, and Functionalization of Untethered Soft Magnetic Robots with Life-Like Motions: A Review

Soft robots have demonstrated superior flexibility and functionality than conventional rigid robots. These versatile devices can respond to a wide range of external stimuli (including light, magnetic field, heat, electric field, etc.), and can perform sophisticated tasks. Notably, soft magnetic robots exhibit unparalleled advantages among numerous soft robots (such as untethered control, rapid response, and high safety), and have made remarkable progress in small-scale manipulation tasks and biomedical applications. Despite the promising potential, soft magnetic robots are still in their infancy and require significant advancements in terms of fabrication, design principles, and functional development to be viable for real-world applications. Recent progress shows that bionics can serve as an effective tool for developing soft robots. In light of this, the review is presented with two main goals: (i) exploring how innovative bioinspired strategies can revolutionize the design and actuation of soft magnetic robots to realize various life-like motions; (ii) examining how these bionic systems could benefit practical applications in small-scale solid/liquid manipulation and therapeutic/diagnostic-related biomedical fields

Humanoid Robots

For many years, the human being has been trying, in all ways, to recreate the complex mechanisms that form the human body. Such task is extremely complicated and the results are not totally satisfactory. However, with increasing technological advances based on theoretical and experimental researches, man gets, in a way, to copy or to imitate some systems of the human body. These researches not only intended to create humanoid robots, great part of them constituting autonomous systems, but also, in some way, to offer a higher knowledge of the systems that form the human body, objectifying possible applications in the technology of rehabilitation of human beings, gathering in a whole studies related not only to Robotics, but also to Biomechanics, Biomimmetics, Cybernetics, among other areas. This book presents a series of researches inspired by this ideal, carried through by various researchers worldwide, looking for to analyze and to discuss diverse subjects related to humanoid robots. The presented contributions explore aspects about robotic hands, learning, language, vision and locomotion

Generating walking behaviours in legged robots

Many legged robots have boon built with a variety of different abilities, from running to liopping to climbing stairs. Despite this however, there has been no consistency of approach to the problem of getting them to walk. Approaches have included breaking down the walking step into discrete parts and then controlling them separately, using springs and linkages to achieve a passive walking cycle, and even working out the necessary movements in simulation and then imposing them on the real robot. All of these have limitations, although most were successful at the task for which they were designed. However, all of them fall into one of two categories: either they alter the dynamics of the robots physically so that the robot, whilst very good at walking, is not as general purpose as it once was (as with the passive robots), or they control the physical mechanism of the robot directly to achieve their goals, and this is a difficult task.In this thesis a design methodology is described for building controllers for 3D dynam¬ ically stable walking, inspired by the best walkers and runners around — ourselves — so the controllers produced are based 011 the vertebrate Central Nervous System. This means that there is a low-level controller which adapts itself to the robot so that, when switched on, it can be considered to simulate the springs and linkages of the passive robots to produce a walking robot, and this now active mechanism is then controlled by a relatively simple higher level controller. This is the best of both worlds — we have a robot which is inherently capable of walking, and thus is easy to control like the passive walkers, but also retains the general purpose abilities which makes it so potentially useful.This design methodology uses an evolutionary algorithm to generate low-level control¬ lers for a selection of simulated legged robots. The thesis also looks in detail at previous walking robots and their controllers and shows that some approaches, including staged evolution and hand-coding designs, may be unnecessary, and indeed inappropriate, at least for a general purpose controller. The specific algorithm used is evolutionary, using a simple genetic algorithm to allow adaptation to different robot configurations, and the controllers evolved are continuous time neural networks. These are chosen because of their ability to entrain to the movement of the robot, allowing the whole robot and network to be considered as a single dynamical system, which can then be controlled by a higher level system.An extensive program of experiments investigates the types of neural models and net¬ work structures which are best suited to this task, and it is shown that stateless and simple dynamic neural models are significantly outperformed as controllers by more complex, biologically plausible ones but that other ideas taken from biological systems, including network connectivities, are not generally as useful and reasons for this are examined.The thesis then shows that this system, although only developed 011 a single robot, is capable of automatically generating controllers for a wide selection of different test designs. Finally it shows that high level controllers, at least to control steering and speed, can be easily built 011 top of this now active walking mechanism

Fondements calculatoires de la locomotion anthropomorphe

La locomotion anthropomorphe est un processus complexe qui met en jeu un très grand nombre de degrés de liberté, le corps humain disposant de plus de trois cents articulations contre une trentaine chez les robots humanoïdes. Pris dans leur ensemble, ces degrés de liberté montrent une certaine cohérence rendant possible la mise en mouvement du système anthropomorphe et le maintien de son équilibre, dans le but d'éviter la chute. Cette thèse met en lumière les fondements calculatoires à l'origine de cette orchestration. Elle introduit un cadre mathématique unifié permettant à la fois l'étude de la locomotion humaine, et la génération de trajectoires locomotrices pour les robots humanoïdes. Ce cadre consiste en une réduction de la dynamique corps-complet du système pour ne considérer que sa projection autour du centre de gravité, aussi appelée dynamique centroïdale. Bien que réduite, nous montrons que cette dynamique centroïdale joue un rôle central dans la compréhension et la formation des mouvements locomoteurs. Pour ce faire, nous établissons dans un premier temps les conditions d'observabilité de cette dynamique, c'est-à-dire que nous montrons dans quelle mesure cette donnée peut être appréhendée à partir des capteurs couramment employés en biomécanique et en robotique. Forts de ces conditions d'observabilité, nous proposons un estimateur capable de reconstruire la position non-biaisée du centre de gravité. A partir de cet estimateur et de l'acquisition de mouvements de marche sur divers sujets, nous mettons en évidence la présence d'un motif cycloïdal du centre de gravité dans le plan sagittal lorsque l'humain marche de manière nominale, c'est-à-dire sans y penser. La présence de ce motif suggère l'existence d'une synergie motrice jusqu'alors ignorée, soutenant la théorie d'une coordination générale des mouvements pendant la locomotion. La dernière contribution de cette thèse porte sur la locomotion multi-contacts. Les humains ont une agilité remarquable pour effectuer des mouvements locomoteurs qui nécessitent l'utilisation conjointe des bras et des jambes, comme lors de l'ascension d'une paroi rocheuse. Comment doter les robots humanoïdes de telles capacités ? La difficulté n'est certainement pas technologique, puisque les robots actuels sont capables de développer des puissances mécaniques suffisantes. Leurs performances, évaluées tant en termes de qualité des mouvements que de temps de calcul, restent très limitées. Dans cette thèse, nous abordons le problème de génération de trajectoires multi-contacts sous la forme d'un problème de commande optimale. L'intérêt de cette formulation est de partir du modèle réduit de la dynamique centroïdale tout en répondant aux contraintes d'équilibre. L'idée originale consiste à maximiser la vraisemblance de cette dynamique réduite vis-à-vis de la dynamique corps-complet. Elle repose sur l'apprentissage d'une mesure d'occupation qui reflète les capacités cinématiques et dynamiques du robot. Elle est effective : l'algorithmique qui en découle est compatible avec des applications temps réel. L'approche a été évaluée avec succès sur le robot humanoïde HRP-2, sur plusieurs modes de locomotions, démontrant ainsi sa polyvalence.Anthropomorphic locomotion is a complex process that involves a very large number of degrees of freedom, the human body having more than three hundred joints against thirty in humanoid robots. Taken as a whole, these degrees of freedom show a certain coherence making it possible to set the anthropomorphic system in motion and maintain its equilibrium, in order to avoid falling. This thesis highlights the computational foundations behind this orchestration. It introduces a unified mathematical framework allowing both the study of human locomotion and the generation of locomotive trajectories for humanoid robots. This framework consists of a reduction of the body-complete dynamics of the system to consider only its projection around the center of gravity, also called centroid dynamics. Although reduced, we show that this centroidal dynamics plays a central role in the understanding and formation of locomotive movements. To do this, we first establish the observability conditions of this dynamic, that is to say that we show to what extent this data can be apprehended from sensors commonly used in biomechanics and robotics. Based on these observability conditions, we propose an estimator able to reconstruct the unbiased position of the center of gravity. From this estimator and the acquisition of walking motions on various subjects, we highlight the presence of a cycloidal pattern of the center of gravity in the sagittal plane when the human is walking nominally, that is, to say without thinking. The presence of this motif suggests the existence of a motor synergy hitherto unknown, supporting the theory of a general coordination of movements during locomotion. The last contribution of this thesis is on multi-contact locomotion. Humans have remarkable agility to perform locomotive movements that require joint use of the arms and legs, such as when climbing a rock wall. How to equip humanoid robots with such capabilities? The difficulty is certainly not technological, since current robots are able to develop sufficient mechanical powers. Their performances, evaluated both in terms of quality of movement and computing time, remain very limited. In this thesis, we address the problem of generating multi-contact trajectories in the form of an optimal control problem. The interest of this formulation is to start from the reduced model of centroid dynamics while responding to equilibrium constraints. The original idea is to maximize the likelihood of this reduced dynamic with respect to body-complete dynamics. It is based on learning a measurement of occupation that reflects the kinematic and dynamic capabilities of the robot. It is effective: the resulting algorithmic is compatible with real-time applications. The approach has been successfully evaluated on the humanoid robot HRP-2, on several modes of locomotion, thus demonstrating its versatility

System Identification of Bipedal Locomotion in Robots and Humans

The ability to perform a healthy walking gait can be altered in numerous cases due to gait disorder related pathologies. The latter could lead to partial or complete mobility loss, which affects the patients’ quality of life. Wearable exoskeletons and active prosthetics have been considered as a key component to remedy this mobility loss. The control of such devices knows numerous challenges that are yet to be addressed. As opposed to fixed trajectories control, real-time adaptive reference generation control is likely to provide the wearer with more intent control over the powered device. We propose a novel gait pattern generator for the control of such devices, taking advantage of the inter-joint coordination in the human gait. Our proposed method puts the user in the control loop as it maps the motion of healthy limbs to that of the affected one. To design such control strategy, it is critical to understand the dynamics behind bipedal walking. We begin by studying the simple compass gait walker. We examine the well-known Virtual Constraints method of controlling bipedal robots in the image of the compass gait. In addition, we provide both the mechanical and control design of an affordable research platform for bipedal dynamic walking. We then extend the concept of virtual constraints to human locomotion, where we investigate the accuracy of predicting lower limb joints angular position and velocity from the motion of the other limbs. Data from nine healthy subjects performing specific locomotion tasks were collected and are made available online. A successful prediction of the hip, knee, and ankle joints was achieved in different scenarios. It was also found that the motion of the cane alone has sufficient information to help predict good trajectories for the lower limb in stairs ascent. Better estimates were obtained using additional information from arm joints. We also explored the prediction of knee and ankle trajectories from the motion of the hip joints

Neuroplastic Changes Following Brain Ischemia and their Contribution to Stroke Recovery: Novel Approaches in Neurorehabilitation

Ischemic damage to the brain triggers substantial reorganization of spared areas and pathways, which is associated with limited, spontaneous restoration of function. A better understanding of this plastic remodeling is crucial to develop more effective strategies for stroke rehabilitation. In this review article, we discuss advances in the comprehension of post-stroke network reorganization in patients and animal models. We first focus on rodent studies that have shed light on the mechanisms underlying neuronal remodeling in the perilesional area and contralesional hemisphere after motor cortex infarcts. Analysis of electrophysiological data has demonstrated brain-wide alterations in functional connectivity in both hemispheres, well beyond the infarcted area. We then illustrate the potential use of non-invasive brain stimulation (NIBS) techniques to boost recovery. We finally discuss rehabilitative protocols based on robotic devices as a tool to promote endogenous plasticity and functional restoration