Evaluating Morphological Computation in Muscle and DC-motor Driven Models of Human Hopping

In the context of embodied artificial intelligence, morphological computation refers to processes which are conducted by the body (and environment) that otherwise would have to be performed by the brain. Exploiting environmental and morphological properties is an important feature of embodied systems. The main reason is that it allows to significantly reduce the controller complexity. An important aspect of morphological computation is that it cannot be assigned to an embodied system per se, but that it is, as we show, behavior- and state-dependent. In this work, we evaluate two different measures of morphological computation that can be applied in robotic systems and in computer simulations of biological movement. As an example, these measures were evaluated on muscle and DC-motor driven hopping models. We show that a state-dependent analysis of the hopping behaviors provides additional insights that cannot be gained from the averaged measures alone. This work includes algorithms and computer code for the measures.Comment: 10 pages, 4 figures, 1 table, 5 algorithm

Effective Viscous Damping Enables Morphological Computation in Legged Locomotion

Muscle models and animal observations suggest that physical damping is beneficial for stabilization. Still, only a few implementations of mechanical damping exist in compliant robotic legged locomotion. It remains unclear how physical damping can be exploited for locomotion tasks, while its advantages as sensor-free, adaptive force- and negative work-producing actuators are promising. In a simplified numerical leg model, we studied the energy dissipation from viscous and Coulomb damping during vertical drops with ground-level perturbations. A parallel spring-damper is engaged between touch-down and mid-stance, and its damper auto-disengages during mid-stance and takeoff. Our simulations indicate that an adjustable and viscous damper is desired. In hardware we explored effective viscous damping and adjustability and quantified the dissipated energy. We tested two mechanical, leg-mounted damping mechanisms; a commercial hydraulic damper, and a custom-made pneumatic damper. The pneumatic damper exploits a rolling diaphragm with an adjustable orifice, minimizing Coulomb damping effects while permitting adjustable resistance. Experimental results show that the leg-mounted, hydraulic damper exhibits the most effective viscous damping. Adjusting the orifice setting did not result in substantial changes of dissipated energy per drop, unlike adjusting damping parameters in the numerical model. Consequently, we also emphasize the importance of characterizing physical dampers during real legged impacts to evaluate their effectiveness for compliant legged locomotion

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

Fast Sensing and Adaptive Actuation for Robust Legged Locomotion

Robust legged locomotion in complex terrain demands fast perturbation detection and reaction. In animals, due to the neural transmission delays, the high-level control loop involving the brain is absent from mitigating the initial disturbance. Instead, the low-level compliant behavior embedded in mechanics and the mid-level controllers in the spinal cord are believed to provide quick response during fast locomotion. Still, it remains unclear how these low- and mid-level components facilitate robust locomotion. This thesis aims to identify and characterize the underlining elements responsible for fast sensing and actuation. To test individual elements and their interplay, several robotic systems were implemented. The implementations include active and passive mechanisms as a combination of elasticities and dampers in multi-segment robot legs, central pattern generators inspired by intraspinal controllers, and a synthetic robotic version of an intraspinal sensor. The first contribution establishes the notion of effective damping. Effective damping is defined as the total energy dissipation during one step, which allows quantifying how much ground perturbation is mitigated. Using this framework, the optimal damper is identified as viscous and tunable. This study paves the way for integrating effective dampers to legged designs for robust locomotion. The second contribution introduces a novel series elastic actuation system. The proposed system tackles the issue of power transmission over multiple joints, while featuring intrinsic series elasticity. The design is tested on a hopper with two more elastic elements, demonstrating energy recuperation and enhanced dynamic performance. The third contribution proposes a novel tunable damper and reveals its influence on legged hopping. A bio-inspired slack tendon mechanism is implemented in parallel with a spring. The tunable damping is rigorously quantified on a central-pattern-generator-driven hopping robot, which reveals the trade-off between locomotion robustness and efficiency. The last contribution explores the intraspinal sensing hypothesis of birds. We speculate that the observed intraspinal structure functions as an accelerometer. This accelerometer could provide fast state feedback directly to the adjacent central pattern generator circuits, contributing to birds’ running robustness. A biophysical simulation framework is established, which provides new perspectives on the sensing mechanics of the system, including the influence of morphologies and material properties. Giving an overview of the hierarchical control architecture, this thesis investigates the fast sensing and actuation mechanisms in several control layers, including the low-level mechanical response and the mid-level intraspinal controllers. The contributions of this work provide new insight into animal loco-motion robustness and lays the foundation for future legged robot design

Morphological properties of mass-spring networks for optimal locomotion learning

Robots have proven very useful in automating industrial processes. Their rigid components and powerful actuators, however, render them unsafe or unfit to work in normal human environments such as schools or hospitals. Robots made of compliant, softer materials may offer a valid alternative. Yet, the dynamics of these compliant robots are much more complicated compared to normal rigid robots of which all components can be accurately controlled. It is often claimed that, by using the concept of morphological computation, the dynamical complexity can become a strength. On the one hand, the use of flexible materials can lead to higher power efficiency and more fluent and robust motions. On the other hand, using embodiment in a closed-loop controller, part of the control task itself can be outsourced to the body dynamics. This can significantly simplify the additional resources required for locomotion control. To this goal, a first step consists in an exploration of the trade-offs between morphology, efficiency of locomotion, and the ability of a mechanical body to serve as a computational resource. In this work, we use a detailed dynamical model of a Mass–Spring–Damper (MSD) network to study these trade-offs. We first investigate the influence of the network size and compliance on locomotion quality and energy efficiency by optimizing an external open-loop controller using evolutionary algorithms. We find that larger networks can lead to more stable gaits and that the system’s optimal compliance to maximize the traveled distance is directly linked to the desired frequency of locomotion. In the last set of experiments, the suitability of MSD bodies for being used in a closed loop is also investigated. Since maximally efficient actuator signals are clearly related to the natural body dynamics, in a sense, the body is tailored for the task of contributing to its own control. Using the same simulation platform, we therefore study how the network states can be successfully used to create a feedback signal and how its accuracy is linked to the body size

Neuro-musculoskeletal Models: A Tool to Study the Contribution of Muscle Dynamics to Biological Motor Control

Das Verständnis der Prinzipien, die menschlichen Bewegungen zugrunde liegen, ist die Basis für die Untersuchung der Entstehung gesunder Bewegungen und, was noch wichtiger ist, der Entstehung motorischer Störungen aufgrund neurodegenerativer Erkrankungen oder anderer pathologischer Zustände. Dieses Verständnis zu erlangen ist jedoch herausfordernd, da menschliche Bewegung das Ergebnis eines komplexen, dynamischen Zusammenspiels von biochemischen und biophysikalischen Prozessen im Bewegungsapparat und den hierarchisch organisierten neuronalen Kontrollstrukturen ist. Um die Wechselwirkungen dieser Strukturen zu untersuchen, bieten Computersimulationen, die mathematische Modelle des muskuloskelettalen Systems mit Modellen seiner neuronalen Kontrolle kombinieren, ein nützliches Werkzeug. In diesen Simulationen können einzelne Prozesse oder ganze Funktionseinheiten deaktiviert oder gestört werden, um die Auswirkungen dieser Veränderungen auf die vorhergesagten Bewegungen zu untersuchen. Die Plausibilität der zugrundeliegenden Modelle kann durch den Vergleich der Simulationen mit Daten aus Humanexperimenten und biologisch inspirierten Robotermodellen beurteilt werden. Das Ziel dieser Arbeit war es, neuro-muskuloskelettale Modelle als Hilfsmittel zur Untersuchung von Konzepten der biologischen Bewegungskontrolle zu verwenden. Von besonderem Interesse war der Beitrag der Muskeldynamik zur Kontrolle, d.h. wie die intrinsischen muskuloskelettalen Eigenschaften die motorische Kontrolle vereinfachen, ohne die motorische Genauigkeit zu beeinträchtigen. Zusätzlich wurde der Einfluss propriozeptiver Reflexmechanismen in verschiedenen Szenarien getestet. Die verwendeten neuro-muskuloskelettalen Modelle sind eine Kombination von Mehrkörpermodellen der Muskel-Skelett-Struktur des Armes oder des ganzen Körpers mit einem biologisch inspirierten hybriden Gleichgewichtspunkt-Kontrollmodell. In einer Simulationsstudie stellten wir fest, dass unser Armmodell realistische Reaktionen auf externe mechanische Störungen für zielgerichtete Bewegungen mit einem Freiheitsgrad vorhersagt. Auf dieser Grundlage simulierten wir die Anwendung von tragbaren Assistenzgeräten zur Kompensation unerwünschter Hypermetrie, d.h. einer überschießenden Reaktion bei zielgerichteten Bewegungen im Zusammenhang mit zerebellärer Ataxie und anderen neurodegenerativen Erkrankungen. Wir fanden heraus, dass einfache mechanische Hilfsmittel ausreichend sein können, um die Hypermetrien auf ein normales Niveau zu reduzieren. Wir stellten jedoch auch fest, dass die Größe des Drehmoments und der Kraft, die zur Kompensation der Störung erforderlich sind, möglicherweise deutlich unterschätzt wird, wenn die Muskel-Sehnen-Eigenschaften im Modell nicht berücksichtigt werden. Die Ergebnisse dieser beiden Studien bestätigten die Hypothese aus der Literatur, dass die Morphologie des Muskel-Skelett-Systems signifikant zur Bewegung beiträgt und somit deren Kontrolle vereinfacht. Deshalb haben wir einen informationstheoretischen Ansatz verwendet, um diesen Beitrag für zielgerichtete und oszillatorische Armbewegungen mit zwei Freiheitsgraden zu charakterisieren. Die Ergebnisse bestätigten, dass die unteren Kontrollebenen, einschließlich der Muskeln und ihrer Aktivierungsdynamik, wichtige Beiträge zur gesamten Kontrollhierarchie leisten. Beispielsweise führt ein einfaches, stückweise konstantes Muskelstimulationssignal, das nur wenig Information enthält, zu einer geschmeidigen Bewegung. Der physiologische Detailgrad, der in unseren Muskel-Skelett-Modellen enthalten ist, ermöglicht nicht nur die Untersuchung von Theorien zur motorischen Kontrolle, sondern auch die Untersuchung von Größen wie inneren Kräften in Muskeln und Gelenken, die experimentell normalerweise nicht zugänglich sind. Diese Größen sind zum Beispiel in der Ergonomie und für die Entwicklung von Assistenzgeräten von Bedeutung. In einer Ganzkörpersimulationsstudie untersuchten wir den Beitrag des Dehnungsreflexes zu den resultierenden Muskelkräften bei einer aktiven externen Repositionierung des Hüftgelenkes für einen großen Bereich von Bewegungsgeschwindigkeiten. Wir fanden heraus, dass der relative Kraftbeitrag des Feedback-Mechanismus vom modellierten kognitiven Zustand abhängig ist und einen nicht vernachlässigbaren Beitrag leistet, insbesondere bei hohen Repositionsgeschwindigkeiten. Die Gesamtheit unserer Ergebnisse zeigt, dass die Eigenschaften des Bewegungsapparates signifikant zur Erzeugung und Kontrolle von Bewegung beitragen und es daher wichtig ist, sie bei der Modellierung der menschlichen Bewegung zu berücksichtigen. Daher sprechen die Ergebnisse für die Kombination eines physiologisch fundierten biomechanischen und biochemischen Modells des Bewegungsapparates mit biologisch inspirierten Konzepten der motorischen Kontrolle. Diese Computersimulationen haben sich als ein nützliches Werkzeug zum Verständnis der Prozesse erwiesen, die der Erzeugung gesunder und pathologisch beeinträchtigter menschlicher Bewegungen zugrunde liegen.Understanding the principles underlying human movement is the basis for investigating the generation of healthy movements and, more importantly, the origins of motor disorders due to neurodegenerative diseases or other pathological conditions. However, gaining this understanding is challenging since human motion is the result of a complex, dynamic interplay of biochemical and biophysical processes in the musculoskeletal system and the hierarchically organized neuronal control structures. To study the interactions of these structures, computer simulations that combine mathematical models of the musculoskeletal system with models of its neuronal control provide a useful tool. In these simulations, single processes or whole functional units can be disabled or perturbed to study the effects of these changes on the predicted movements. The plausibility of the underlying models can be assessed by comparing the simulations with data from human experiments and biologically inspired robotic models. The purpose of this work was to use neuro-musculoskeletal models as tools to study concepts of biological motor control. Of particular interest was the contribution of muscle dynamics to the control, i.e. how the intrinsic musculoskeletal properties simplify motor control without compromising motor accuracy. Additionally, the influence of proprioceptive reflex mechanisms was tested in different scenarios. The neuro-musculoskeletal models that were used are a combination of multibody musculoskeletal models of the arm or the whole body with a biologically inspired hybrid equilibrium-point controller. In a simulation study, we found that our arm model predicts realistic reactions to external mechanical perturbations while performing one-degree-of-freedom goal-directed movements. Based on this, we simulated the application of wearable assistive devices to compensate for unwanted hypermetria, i.e. an overshooting response in goal-directed movements associated with cerebellar ataxia and other neurodegenerative disorders. We found that simple mechanical devices may be sufficient to reduce the hypermetria to a normal level. However, we also observed that the magnitude of torque and power that is required to compensate for the disorder may be significantly underestimated if muscle-tendon characteristics are not considered in the computational model. The results of these two studies confirmed the hypothesis from literature that the morphology of musculoskeletal systems significantly contributes to the movement and thus simplifies its control. Therefore, we made use of the information-theoretic approach of quantifying morphological computation to characterize this contribution for goal-directed and oscillatory arm movements with two degrees of freedom. The results asserted that the lower levels of control, including the muscles and their activation dynamics, make important contributions to the overall control hierarchy. For example, a simple piecewise constant muscle stimulation signal that contains only little information results in a smooth movement. The level of physiological detail that is included in our musculoskeletal models does not only allow for the examination of motor control theories but also makes it possible to study quantities like internal forces in muscles and joints, usually not experimentally accessible. These quantities are relevant, for example, in ergonomics and for the development of assistive devices. In a whole-body simulation study, we investigated the contribution of the stretch reflex to the resulting muscle forces during active external repositioning of the hip joint for a large range of movement velocities. We found that, depending on the modeled cognitive state, the relative force contribution of the feedback mechanism is not negligible, especially for high repositioning velocities. The entirety of our results shows that the properties of the musculoskeletal system significantly contribute to the generation and control of movement and, thus, it is important to take them into account when modeling human movement. Therefore, the results advocate the combination of a physiologically well-founded biomechanical and biochemical model of the musculoskeletal system with biologically inspired concepts of motor control. These computer simulations have proven to be a useful tool towards the comprehension of the processes underlying the generation of healthy and pathologically impaired human movements

Understanding and Improving Locomotion: The Simultaneous Optimization of Motion and Morphology in Legged Robots

There exist many open design questions in the field of legged robotics. Should leg extension and retraction occur with a knee or a prismatic joint? Will adding a compliant ankle lead to improved energetics compared to a point foot? Should quadrupeds have a flexible or a rigid spine? Should elastic elements in the actuation be placed in parallel or in series with the motors? Though these questions may seem basic, they are fundamentally difficult to approach. A robot with either discrete choice will likely need very different components and use very different motion to perform at its best. To make a fair comparison between two design variations, roboticists need to ask, is the best version of a robot with a discrete morphological variation better than the best version of a robot with the other variation? In this dissertation, I propose to answer these type of questions using an optimization based approach. Using numerical algorithms, I let a computer determine the best possible motion and best set of parameters for each design variation in order to be able to compare the best instance of each variation against each other. I developed and implemented that methodology to explore three primary robotic design questions. In the first, I asked if parallel or series elastic actuation is the more energetically economical choice for a legged robot. Looking at a variety of force and energy based cost functions, I mapped the optimal motion cost landscape as a function of configurable parameters in the hoppers. In the best case, the series configuration was more economical for an energy based cost function, and the parallel configuration was better for a force based cost function. I then took this work a step further and included the configurable parameters directly within the optimization on a model with gear friction. I found, for the most realistic cost function, the electrical work, that series was the better choice when the majority of the transmission was handled by a low-friction rotary-to-linear transmission. In the second design question, I extended this analysis to a two-dimensional monoped moving at a forward velocity with either parallel or series elastic actuation at the hip and leg. In general it was best to have a parallel elastic actuator at the hip, and a series elastic actuator at the leg. In the third design question, I asked if there is an energetic benefit to having an articulated spinal joint instead of a rigid spinal joint in a quadrupedal legged robot. I found that the answer was gait dependent. For symmetrical gaits, such as walking and trotting, the rigid and articulated spine models have similar energetic economy. For asymmetrical gaits, such as bounding and galloping, the articulated spine led to significant energy savings at high speeds. The combination of the above studies readily presents a methodology for simultaneously optimizing for motion and morphology in legged robots. Aside from giving insight into these specific design questions, the technique can also be extended to a variety of other design questions. The explorations in turn inform future hardware development by roboticists and help explain why animals in nature move in the ways that they do.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144074/1/yevyes_1.pd

Description of motor control using inverse models

Humans can perform complicated movements like writing or running without giving them much thought. The scientific understanding of principles guiding the generation of these movements is incomplete. How the nervous system ensures stability or compensates for injury and constraints – are among the unanswered questions today. Furthermore, only through movement can a human impose their will and interact with the world around them. Damage to a part of the motor control system can lower a person’s quality of life. Understanding how the central nervous system (CNS) forms control signals and executes them helps with the construction of devices and rehabilitation techniques. This allows the user, at least in part, to bypass the damaged area or replace its function, thereby improving their quality of life. CNS forms motor commands, for example a locomotor velocity or another movement task. These commands are thought to be processed through an internal model of the body to produce patterns of motor unit activity. An example of one such network in the spinal cord is a central pattern generator (CPG) that controls the rhythmic activation of synergistic muscle groups for overground locomotion. The descending drive from the brainstem and sensory feedback pathways initiate and modify the activity of the CPG. The interactions between its inputs and internal dynamics are still under debate in experimental and modelling studies. Even more complex neuromechanical mechanisms are responsible for some non-periodic voluntary movements. Most of the complexity stems from internalization of the body musculoskeletal (MS) system, which is comprised of hundreds of joints and muscles wrapping around each other in a sophisticated manner. Understanding their control signals requires a deep understanding of their dynamics and principles, both of which remain open problems. This dissertation is organized into three research chapters with a bottom-up investigation of motor control, plus an introduction and a discussion chapter. Each of the three research chapters are organized as stand-alone articles either published or in preparation for submission to peer-reviewed journals. Chapter two introduces a description of the MS kinematic variables of a human hand. In an effort to simulate human hand motor control, an algorithm was defined that approximated the moment arms and lengths of 33 musculotendon actuators spanning 18 degrees of freedom. The resulting model could be evaluated within 10 microseconds and required less than 100 KB of memory. The structure of the approximating functions embedded anatomical and functional features of the modelled muscles, providing a meaningful description of the system. The third chapter used the developments in musculotendon modelling to obtain muscle activity profiles controlling hand movements and postures. The agonist-antagonist coactivation mechanism was responsible for producing joint stability for most degrees of freedom, similar to experimental observations. Computed muscle excitations were used in an offline control of a myoelectric prosthesis for a single subject. To investigate the higher-order generation of control signals, the fourth chapter describes an analytical model of CPG. Its parameter space was investigated to produce forward locomotion when controlled with a desired speed. The model parameters were varied to produce asymmetric locomotion, and several control strategies were identified. Throughout the dissertation the balance between analytical, simulation, and phenomenological modelling for the description of simple and complex behavior is a recurrent theme of discussion

Data-Driven Methods to Build Robust Legged Robots

For robots to ever achieve signicant autonomy, they need to be able to mitigate performance loss due to uncertainty, typically from a novel environment or morphological variation of their bodies. Legged robots, with their complex dynamics, are particularly challenging to control with principled theory. Hybrid events, uncertainty, and high dimension are all confounding factors for direct analysis of models. On the other hand, direct data-driven methods have proven to be equally dicult to employ. The high dimension and mechanical complexity of legged robots have proven challenging for hardware-in-the-loop strategies to exploit without signicant eort by human operators. We advocate that we can exploit both perspectives by capitalizing on qualitative features of mathematical models applicable to legged robots, and use that knowledge to strongly inform data-driven methods. We show that the existence of these simple structures can greatly facilitate robust design of legged robots from a data-driven perspective. We begin by demonstrating that the factorial complexity of hybrid models can be elegantly resolved with computationally tractable algorithms, and establish that a novel form of distributed control is predicted. We then continue by demonstrating that a relaxed version of the famous templates and anchors hypothesis can be used to encode performance objectives in a highly redundant way, allowing robots that have suffered damage to autonomously compensate. We conclude with a deadbeat stabilization result that is quite general, and can be determined without equations of motion.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155053/1/gcouncil_1.pd

Modular Hopping and Running via Parallel Composition

Though multi-functional robot hardware has been created, the complexity in its functionality has been constrained by a lack of algorithms that appropriately manage flexible and autonomous reconfiguration of interconnections to physical and behavioral components. Raibert pioneered a paradigm for the synthesis of planar hopping using a composition of ``parts\u27\u27: controlled vertical hopping, controlled forward speed, and controlled body attitude. Such reduced degree-of-freedom compositions also seem to appear in running animals across several orders of magnitude of scale. Dynamical systems theory can offer a formal representation of such reductions in terms of ``anchored templates,\u27\u27 respecting which Raibert\u27s empirical synthesis (and the animals\u27 empirical performance) can be posed as a parallel composition. However, the orthodox notion (attracting invariant submanifold with restriction dynamics conjugate to a template system) has only been formally synthesized in a few isolated instances in engineering (juggling, brachiating, hexapedal running robots, etc.) and formally observed in biology only in similarly limited contexts. In order to bring Raibert\u27s 1980\u27s work into the 21st century and out of the laboratory, we design a new family of one-, two-, and four-legged robots with high power density, transparency, and control bandwidth. On these platforms, we demonstrate a growing collection of $\{$body, behavior$\}$ pairs that successfully embody dynamical running / hopping ``gaits\u27\u27 specified using compositions of a few templates, with few parameters and a great deal of empirical robustness. We aim for and report substantial advances toward a formal notion of parallel composition---embodied behaviors that are correct by design even in the presence of nefarious coupling and perturbation---using a new analytical tool (hybrid dynamical averaging). With ideas of verifiable behavioral modularity and a firm understanding of the hardware tools required to implement them, we are closer to identifying the components required to flexibly program the exchange of work between machines and their environment. Knowing how to combine and sequence stable basins to solve arbitrarily complex tasks will result in improved foundations for robotics as it goes from ad-hoc practice to science (with predictive theories) in the next few decades