Soft Robotics: Cerebellar Inspired Control of Artificial Muscles

Soft robots have the potential to greatly improve human-robot interaction via intrinsically safe, compliant designs. However, new compliant materials used in soft robotics – artificial muscles – are fabricated with poor tolerances and have time-varying dynamics. Therefore, a key technical challenge is to develop adaptive control algorithms for these materials. Here, we take a novel bio-inspired approach to artificial muscle control using the adaptive filter model of the cerebellum. The cerebellum is a brain structure essential for fine-tuning human performance in a diverse range of sensory and motor tasks. Its ability to automatically calibrate and adapt to changes in a wide variety of systems using a homogenous, repeating structure suggests that cerebellar-inspired models are highly suited to controlling artificial muscles in a range of tasks. We investigate the performance of the cerebellar adaptive filter algorithm in the displacement control of a soft actuator. Experimental results demonstrate that the cerebellar algorithm is successful and learns to accurately control the time-varying dynamics of the soft actuator in real-time

Cerebellar-inspired algorithm for adaptive control of nonlinear dielectric elastomerbased artificial muscle

© 2016 The Author(s) Published by the Royal Society. All rights reserved. Electroactive polymer actuators are important for soft robotics, but can be difficult to control because of compliance, creep and nonlinearities. Because biological control mechanisms have evolved to deal with such problems, we investigated whether a control scheme based on the cerebellum would be useful for controlling a nonlinear dielectric elastomer actuator, a class of artificial muscle. The cerebellum was represented by the adaptive filter model, and acted in parallel with a brainstem, an approximate inverse plant model. The recurrent connections between the two allowed for direct use of sensory error to adjust motor commands. Accurate tracking of a displacement command in the actuator's nonlinear range was achieved by either semi-linear basis functions in the cerebellar model or semi-linear functions in the brainstem corresponding to recruitment in biological muscle. In addition, allowing transfer of training between cerebellum and brainstem as has been observed in the vestibulo-ocular reflex prevented the steady increase in cerebellar output otherwise required to deal with creep. The extensibility and relative simplicity of the cerebellar-based adaptive-inverse control scheme suggests that it is a plausible candidate for controlling this type of actuator. Moreover, its performance highlights important features of biological control, particularly nonlinear basis functions, recruitment and transfer of training

A McKibben muscle arm learning equilibrium postures

In designing artificial systems for studying motor control in humans and other organisms a key point to consider is the complexity reached by brain and body in their developmental stages. An artificial system whose brain and body complexity is shaped according to developmental stages might allow understanding weather, for example, newborn infants, infants, and adults use different neural mechanisms to cope with the same motor control problems. This article proposes an artificial system which aims at becoming a tool to study this type of problems. The system has a brain and body endowed with a set of minimal bio-mimetic features: (a) neural maps activated by receptive fields; (b) connections plasticity changed by Hebbian rule; (c) robotic arm actuated by a McKibben muscle. The arm autonomously learns to reach specific positions in space under the effect of gravity and for different load conditions. The results suggest that a fast and incremental goalaction mapping formation could constitute the computational mechanism underlying the neural growth and plasticity of an early developed brain at the onset of reaching. The same mechanism also allows a first approximate solution for load compensation avoiding the use of more sophisticated internal models (developed in further brain and body developmental stages). This paper aims to be a preliminary study on the feasibility of this approach

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

A multizone cerebellar chip for bioinspired adaptive robot control and sensorimotor processing:A multizone cerebellar chip for bioinspired adaptive robot control and sensorimotor processing

The cerebellum is a neural structure essential for learning, which is connected via multiple zones to many different regions of the brain, and is thought to improve human performance in a large range of sensory, motor and even cognitive processing tasks. An intriguing possibility for the control of complex robotic systems would be to develop an artificial cerebellar chip with multiple zones that could be similarly connected to a variety of subsystems to optimize performance. The novel aim of this paper, therefore, is to propose and investigate a multizone cerebellar chip applied to a range of tasks in robot adaptive control and sensorimotor processing. The multizone cerebellar chip was evaluated using a custom robotic platform consisting of an array of tactile sensors driven by dielectric electroactive polymers mounted upon a standard industrial robot arm. The results demonstrate that the performance in each task was improved by the concurrent, stable learning in each cerebellar zone. This paper, therefore, provides the first empirical demonstration that a synthetic, multizone, cerebellar chip could be embodied within existing robotic systems to improve performance in a diverse range of tasks, much like the cerebellum in a biological system.</p

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-

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

Adaptive, fast walking in a biped robot under neuronal control and learning

Human walking is a dynamic, partly self-stabilizing process relying on the interaction of the biomechanical design with its neuronal control. The coordination of this process is a very difficult problem, and it has been suggested that it involves a hierarchy of levels, where the lower ones, e.g., interactions between muscles and the spinal cord, are largely autonomous, and where higher level control (e.g., cortical) arises only pointwise, as needed. This requires an architecture of several nested, sensori–motor loops where the walking process provides feedback signals to the walker's sensory systems, which can be used to coordinate its movements. To complicate the situation, at a maximal walking speed of more than four leg-lengths per second, the cycle period available to coordinate all these loops is rather short. In this study we present a planar biped robot, which uses the design principle of nested loops to combine the self-stabilizing properties of its biomechanical design with several levels of neuronal control. Specifically, we show how to adapt control by including online learning mechanisms based on simulated synaptic plasticity. This robot can walk with a high speed (>3.0 leg length/s), self-adapting to minor disturbances, and reacting in a robust way to abruptly induced gait changes. At the same time, it can learn walking on different terrains, requiring only few learning experiences. This study shows that the tight coupling of physical with neuronal control, guided by sensory feedback from the walking pattern itself, combined with synaptic learning may be a way forward to better understand and solve coordination problems in other complex motor tasks

Adaptive Filter Model of Cerebellum for Biological Muscle Control With Spike Train Inputs

Prior applications of the cerebellar adaptive filter model have included a range of tasks within simulated and robotic systems. However, this has been limited to systems driven by continuous signals. Here, the adaptive filter model of the cerebellum is applied to the control of a system driven by spiking inputs by considering the problem of controlling muscle force. The performance of the standard adaptive filter algorithm is compared with the algorithm with a modified learning rule that minimizes inputs and a simple proportional-integral-derivative (PID) controller. Control performance is evaluated in terms of the number of spikes, the accuracy of spike input locations, and the accuracy of muscle force output. Results show that the cerebellar adaptive filter model can be applied without change to the control of systems driven by spiking inputs. The cerebellar algorithm results in good agreement between input spikes and force outputs and significantly improves on a PID controller. Input minimization can be used to reduce the number of spike inputs, but at the expense of a decrease in accuracy of spike input location and force output. This work extends the applications of the cerebellar algorithm and demonstrates the potential of the adaptive filter model to be used to improve functional electrical stimulation muscle control

Error minimising gradients for improving cerebellar model articulation controller performance

In motion control applications where the desired trajectory velocity exceeds an actuator’s maximum velocity limitations, large position errors will occur between the desired and actual trajectory responses. In these situations standard control approaches cannot predict the output saturation of the actuator and thus the associated error summation cannot be minimised.An adaptive feedforward control solution such as the Cerebellar Model Articulation Controller (CMAC) is able to provide an inherent level of prediction for these situations, moving the system output in the direction of the excessive desired velocity before actuator saturation occurs. However the pre-empting level of a CMAC is not adaptive, and thus the optimal point in time to start moving the system output in the direction of the excessive desired velocity remains unsolved. While the CMAC can adaptively minimise an actuator’s position error, the minimisation of the summation of error over time created by the divergence of the desired and actual trajectory responses requires an additional adaptive level of control.This thesis presents an improved method of training CMACs to minimise the summation of error over time created when the desired trajectory velocity exceeds the actuator’s maximum velocity limitations. This improved method called the Error Minimising Gradient Controller (EMGC) is able to adaptively modify a CMAC’s training signal so that the CMAC will start to move the output of the system in the direction of the excessive desired velocity with an optimised pre-empting level.The EMGC was originally created to minimise the loss of linguistic information conveyed through an actuated series of concatenated hand sign gestures reproducing deafblind sign language. The EMGC concept however is able to be implemented on any system where the error summation associated with excessive desired velocities needs to be minimised, with the EMGC producing an improved output approximation over using a CMAC alone.In this thesis, the EMGC was tested and benchmarked against a feedforward / feedback combined controller using a CMAC and PID controller. The EMGC was tested on an air-muscle actuator for a variety of situations comprising of a position discontinuity in a continuous desired trajectory. Tested situations included various discontinuity magnitudes together with varying approach and departure gradient profiles.Testing demonstrated that the addition of an EMGC can reduce a situation’s error summation magnitude if the base CMAC controller has not already provided a prior enough pre-empting output in the direction of the situation. The addition of an EMGC to a CMAC produces an improved approximation of reproduced motion trajectories, not only minimising position error for a single sampling instance, but also over time for periodic signals