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Mechanical laws of motion were applied very early for better understanding anthropomorphic action as suggested in advance by Newton «For from hence are easily deduced the forces of machines, which are compounded of wheels, pullies, levers, cords, and weights, ascending directly or obliquely, and other mechanical powers; as also the force of the tendons to move the bones of animals». In the 19th century E.J. Marey and E. Muybridge introduced chronophotography to scientifically investigate animal and human movements. They opened the field of motion analysis by being the first scientists to correlate ground reaction forces with kinetics.

Despite of the apparent simplicity of a given skilled movement, the organization of the underlying neuro-musculo-skeletal system remains unknown. A reason is the redundancy of the motor system: a given action can be realized by different muscle and joint activity patterns, and the same underlying activity may give rise to several movements. After the pioneering work of N. Bernstein in the 60’s on the existence of motor synergies, numerous researchers «walking on the border» of their disciplines tend to discover laws and principles underlying the human motions and how the brain reduces the redundancy of the system. These synergies represent the fundamental building blocks composing complex movements.

In robotics, researchers face the same redundancy and complexity challenges as the researchers in life sciences. This book gathers works of roboticists and researchers in biomechanics in order to promote an interdisciplinary research on anthropomorphic systems at large and on humanoid robotics in particular.




Biomechanics studies the understanding human body motions: how to describe them, analyze them and assess them. Biomechanics as an interdisciplinary field can date back to Aristotle (384–322–BC) who wrote the treatise “About the movement of Animals” (350 BC) and provided the first analysis of gait. Later, Herophilos (335–280 BC), Galen.
Jean-Paul Laumond, Gentiane Venture, Bruno Watier

Should Anthropomorphic Systems be “Redundant”?

We explore the conceptual design and implementation of muscle redundancy and kinematic redundancy for anthropomorphic robots from three perspectives: (i) The control of tendon-driven systems, (ii) How the number of muscles define functional capabilities, and (iii) How too few synergies can be detrimental to functional versatility. Historically, roboticists prefer either rotational actuators located at each joint (i.e., rotational degree-of-freedom, DOF), or few linear actuators (i.e., two dedicated muscles per joint) for tendon-driven robots. In contrast, biological limbs have evolved to include too many muscles (Valero-Cuevas in Fundamentals of neuromechanics. Springer, Berlin (2015) [1]), which are thought to unnecessarily complicate their anatomy and control. The question, then, is why has evolution converged on these apparently under-determined (or redundant) solutions? If we really have extra muscles, then which muscle would you give up? By taking a formal mathematical approach to the control of tendons—which is the actual problem that confronts the nervous system—we have proposed a resolution to this apparent paradox by proposing that vertebrates may have, in fact, barely enough muscles to meet the numerous physical constraints for ecological functions (as opposed to simple laboratory tasks) (Valero-Cuevas in Fundamentals of neuromechanics. Springer, Berlin (2015) [1]; Loeb in Overcomplete musculature or underspecified tasks? Mot Control 4(1):81–83 (2000) [2]). This approach can be called Feasibility Theory, which describes how the anatomy of the system, and the constraints defining the task define the set of feasible actions the system can produce. The role of the (neural or engineered) controller is then, to find ways to use the mechanical capabilities of the combined controller-plant system to the fullest (Valero-Cuevas in Fundamentals of neuromechanics. Springer, Berlin (2015) [1]. Similarly, the effective mechanical design of a robotic limb, at a minimum, requires controllability (i.e., enough control degrees of freedom, or muscles) to produce arbitrary forces and movements (i.e., changes of state; Ogata in Modern control engineering. Prentice hall, India (2002) [3]). Force and movement capabilities have distinct governing equations and are, in fact, in competition with one another (e.g., a see-saw demonstrates, as per the Law of Conservation of Energy, how producing higher forces is associated with lower velocities and vice versa). Therefore, we explored the potential evolutionary pressures that may have shaped vertebrate limbs by evaluating how the number of muscles affects the competing demands to produce endpoint forces and velocities. A related concept that cuts across biological and robotic systems is the idea that the kinematics and kinetics of a wide variety of actions exhibit a low-dimensional structure that can be approximated with a few principal components (sometimes called descriptive synergies; Brock and Valero-Cuevas in Transferring synergies from neuroscience to robotics comment on hand synergies: integration of robotics and neuroscience for understanding the control of biological and artificial hands by M. Santello et al. Phys Life Rev 17:27 (2016) [4]; Rieffel et al. in Automated discovery and optimization of large irregular tensegrity structures. Comput Struct 87(5):368–379 (2009) [5]). This has been taken to mean that a few degrees of freedom suffice to produce versatile behavior in the real world. However, the fine behavioral details that distinguish different actions are, by definition, not captured by the commonalities among them. Thus, versatility in the real world likely depends on recognizing and executing fine distinctions among actions; which implies that more degrees of freedom of control are critical for true functional versatility. These three independent arguments support the perspective that creating anthropomorphic systems requires apparently redundant structures, because only then can they truly execute a wide variety of real-world tasks. In addition, we also present an open-access MATLAB toolbox that allows users from different backgrounds to explore these concepts in detail. We believe this new perspective will improve the conceptualization, understanding, and design of anthropomorphic systems.
Ali Marjaninejad, Francisco J. Valero-Cuevas

From Biomechanics to Robotics

How does the central nervous system select and coordinate different degrees of freedom to execute a given movement? The difficulty is to choose specific motor commands among an infinite number of possible ones. If some invariants of movement can be identified, there exists however considerable variability showing that motor control favors rather an envelope of possible movements than a strong stereotypy. However, the central nervous system is able to find extremely fast solutions to the problem of muscular and kinematic redundancy by producing stable and precise movements. To date, no computational model has made it possible to develop a movement generation algorithm with a performance comparable to that of humans in terms of speed, accuracy, robustness and adaptability. One of the reasons is certainly that correct criteria for the synthesis of movement as a function of the task have not yet been identified and used for motion generation. In this chapter we propose to study highly dynamic human movements taking into account their variability, making the choice to consider performance biomechanical variables (tasks) for generating motions and involving whole-body articulations.
Galo Maldonado, Philippe Souères, Bruno Watier

Multibody Optimisations: From Kinematic Constraints to Knee Contact Forces and Ligament Forces

Musculoskeletal models are widely used in biomechanics today to better understand muscle and joint function. Musculo-tendon forces as well as joint contact forces and ligament forces can be estimated within an inverse dynamics computational framework. Using a musculoskeletal model of the lower limb, this chapter presents the different optimisations required to drive the model with experimental data and to compute these forces and their interactions. In these optimisations, the development of anatomical constraints representing, for example, the medial and lateral tibiofemoral contacts or the cruciate ligaments is crucial both to inverse kinematics and to inverse dynamics. Some emblematic results are presented for knee contact forces and ligament forces during gait, illustrating the couplings between joint degrees of freedom and the interactions between forces acting both in muscles and in joints.
Raphael Dumas, Laurence Cheze, Florent Moissenet

Creating Personalized Dynamic Models

In human motion science, the dynamics plays an important role. It relates the movement of the human to the forces necessary to achieve this movement. It also relates the human and its environment through interaction forces. Estimating subject-specific dynamic models is a challenging problem, due to the need for both accurate measurement and modeling formalisms. In the past decade, we have developed solutions for the computation of the dynamic quantities of humans, based on individual (subject specific) models, inspired largely by Robotics geometric and dynamic calibration. In this chapter, we will present the state of the art and our latest advances in this area and show examples of applications to both humans and humanoid robots. With these research results we hope to contribute beyond the field of robotics to the fields of biomechanics and ergonomics, by providing accurate dynamic models of beings.
G. Venture, V. Bonnet, D. Kulic

Optimality and Modularity in Human Movement: From Optimal Control to Muscle Synergies

In this chapter, we review recent work related to the optimal and modular control hypotheses for human movement. Optimal control theory is often thought to imply that the brain continuously computes global optima for each motor task it faces. Modular control theory typically assumes that the brain explicitly stores genuine synergies in specific neural circuits whose combined recruitment yields task-effective motor inputs to muscles. Put this way, these two influential motor control theories are pushed to extreme positions. A more nuanced view, framed within Marr’s tri-level taxonomy of a computational theory of movement neuroscience, is discussed here. We argue that optimal control is best viewed as helping to understand “why” certain movements are preferred over others but does not say much about how the brain would practically trigger optimal strategies. We also argue that dimensionality reduction found in muscle activities may be a by-product of optimality and cannot be attributed to neurally hardwired synergies stricto sensu, in particular when the synergies are extracted from simple factorization algorithms applied to electromyographic data; their putative nature is indeed strongly dictated by the methodology itself. Hence, more modeling work is required to critically test the modularity hypothesis and assess its potential neural origins. We propose that an adequate mathematical formulation of hierarchical motor control could help to bridge the gap between optimality and modularity, thereby accounting for the most appealing aspects of the human motor controller that robotic controllers would like to mimic: rapidity, efficiency, and robustness.
Bastien Berret, Ioannis Delis, Jérémie Gaveau, Frédéric Jean

Human Movements: Synergies, Stability, and Agility

When people move, they organize large, abundant sets of elements (limbs, joints, digits, muscles, motor units, etc.) in a task–specific way by the central nervous system. Such organizations (synergies) ensure action stability, which is crucial given the varying internal body states and external forces. Action stability is controlled in a task-specific way. In particular, stability is reduced in a feed-forward manner (anticipatory synergy adjustment, ASA) if a person plans to perform a quick change of a salient performance variable. The importance of controlled stability for everyday movements is exemplified by studies of neurological patients who show deficits in both aspects of controlled stability: reduced stability during steady-state actions and small/delayed ASAs in preparation to a quick action. The physical approach to movement synergies has been developed using two theoretical frameworks. One of them is the idea of control with spatial referent coordinates (RCs) for salient variables. The other is the idea of intention-specific stability of redundant systems developed within the uncontrolled manifold (UCM) hypothesis. The RC and UCM hypotheses have been united into a single theory incorporating the idea of hierarchical control. This theory is able to account for results of a variety of studies that used perturbations of ongoing movements, analysis of variance across repetitive trials, and analysis of motor equivalence. Recent studies have provided links of this theory to neurophysiological structures and provide tools sensitive to impaired stability and agility of movements in patients with various neurological disorders.
Mark L. Latash

Motor Compositionality and Timing: Combined Geometrical and Optimization Approaches

Human movements are characterized by their invariant spatiotemporal features. The kinematic features and internal movement timing were accounted for by the mixture of geometries model using a combination of Euclidean, affine and equi-affine geometries. Each geometry defines a unique parametrization along a given curve and the net tangential velocity arises from a weighted summation of the logarithms of the geometric velocities. The model was also extended to deal with geometrical singularities forcing unique constraints on the allowed geometric mixture. Human movements were shown to optimize different costs. Specifically, hand trajectories were found to maximize motion smoothness by minimizing jerk. The minimum jerk model successfully accounted for a range of human end-effector motions including unconstrained and path-constrained trajectories. The two modeling approaches involving motion optimality and the geometries’ mixture model are here further combined to form a joint model whereby specific compositions of geometries can be selected to generate an optimal behavior. The optimization serves to define the timing along a path. Additionally, new notions regarding the nature of movement primitives used for the construction of complex movements naturally arise from the consideration of the two modelling approaches. In particular, we suggest that motion primitives may consist of affine orbits; trajectories arising from the group of full-affine transformations. Affine orbits define the movement’s shape. Particular mixtures of geometries achieve the smoothest possible motions, defining timing along each orbit. Finally, affine orbits can be extracted from measured human paths, enabling movement segmentation and an affine-invariant representation of hand trajectories.
Tamar Flash, Matan Karklinsky, Ronit Fuchs, Alain Berthoz, Daniel Bennequin, Yaron Meirovitch

Review of Anthropomorphic Head Stabilisation and Verticality Estimation in Robots

In many walking, running, flying, and swimming animals, including mammals, reptiles, and birds, the vestibular system plays a central role for verticality estimation and is often associated with a head stabilisation (in rotation) behaviour. Head stabilisation, in turn, subserves gaze stabilisation, postural control, visual-vestibular information fusion and spatial awareness via the active establishment of a quasi-inertial frame of reference. Head stabilisation helps animals to cope with the computational consequences of angular movements that complicate the reliable estimation of the vertical direction. We suggest that this strategy could also benefit free-moving robotic systems, such as locomoting humanoid robots, which are typically equipped with inertial measurements units. Free-moving robotic systems could gain the full benefits of inertial measurements if the measurement units are placed on independently orientable platforms, such as a human-like heads. We illustrate these benefits by analysing recent humanoid robots design and control approaches.
Ildar Farkhatdinov, Hannah Michalska, Alain Berthoz, Vincent Hayward

The Physics and Control of Balancing on a Point in the Plane

This chapter presents a new model of the physical process of balancing in a vertical plane, in which the essential parameters of a robot’s balancing behaviour are distilled into just two numbers, regardless of the complexity of the robot. It also presents a balance control system based on this model, and a simple method for leaning in anticipation of future movements. The result is a control system that requires relatively little computation, yet allows a robot to make large, fast movements without falling over. Furthermore, by seeking to control the balance model only, instead of trying to control the whole robot, one achieves a separation between the potential complexity of the robot’s complete equations of motion and the simplicity of its balancing behaviour. The chapter concludes with a simulation study to illustrate the kind of performance that can be achieved, and a brief discussion on how the theory can be extended to 3D.
Roy Featherstone

Design and Control of a Passive Noise Rejecting Variable Stiffness Actuator

Inspired by the biomechanical and passive properties of human muscles, we present a novel actuator named passive noise rejecting Variable Stiffness Actuator (pnrVSA). For a single actuated joint, the proposed design adopts two motor-gear groups in an agonist-antagonist configuration coupled to the joint via serial non-linear springs. From a mechanical standpoint, the introduced novelty resides in two parallel non-linear springs connecting the internal motor-gear groups to the actuator frame. These additional elastic elements create a closed force path that mechanically attenuates the effects of external noise. We further explore the properties of this novel actuator by modeling the effect of gears static frictions on the output joint equilibrium position during the co-contraction of the agonist and antagonist side of the actuator. As a result, we found an analytical condition on the spring potential energies to guarantee that co-activation reduces the effect of friction on the joint equilibrium position. The design of an optimized set of springs respecting this condition leads to the construction of a prototype of our actuator. To conclude the work, we also present two control solutions that exploit the mechanical design of the actuator allowing to control both the joint stiffness and the joint equilibrium position.
Luca Fiorio, Francesco Romano, Alberto Parmiggiani, Bastien Berret, Giorgio Metta, Francesco Nori

Bipedal Locomotion: A Continuous Tradeoff Between Robustness and Energy-Efficiency

Walking is a mechanical process involving all the limbs the body and subject to balance constraints. The structure of the body can allow the emergence of a stable walking using few to no energy, but this motion is sensitive to external perturbations and prone to fall easily. On the other hand, with strong actuation and anticipation, much more disturbances can be overcome, but at the cost of high energy consumption. The choice between these two modes of locomotion is more than a context-dependent binary selection. It is a continuous tradeoff, not only providing a span of different walking control policies but building the framework of a precise classification of these controllers. In this chapter, we discuss, for the humans and the robots, the two extreme walking paradigms and how to sort the solutions ranging between them. We analyze also the incentives behind the decision to change to a more robust or more efficient control policy. Finally, we show that the versatility of a walker depends deeply on the relationships lying between its controller dynamics and its mechanical design, both being ideally built together in a process of codesign.
Mehdi Benallegue, Jean-Paul Laumond

An Overview of Humanoid Robots Technologies

Humanoid robots are challenging mechatronics structures with several interesting features. Choosing a humanoid robot to develop applications or pursue research in a given direction might be difficult due to the strong interdependence of the technical aspects. This paper aims at giving a general description of this interdependence and highlight the lessons learned from the impressive works conducted in the past decade. The reader will find in the annex a table synthesizing the characteristics of the most relevant humanoid robots. Without focusing on a specific application we consider two main classes of humanoid robots: the ones dedicated to industrial application and the ones dedicated to human-robot interaction. The technical aspects are described in a way which illustrates the humanoid robots bridging the gap between these two classes. Finally this paper tries to make a synthesis on recent technological developments.
O. Stasse, T. Flayols
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