Integration of Gravitational Torques in Cerebellar Pathways Allows for the Dynamic Inverse Computation of Vertical Pointing Movements of a Robot Arm

Several authors suggested that gravitational forces are centrally represented in the brain for planning, control and sensorimotor predictions of movements. Furthermore, some studies proposed that the cerebellum computes the inverse dynamics (internal inverse model) whereas others suggested that it computes sensorimotor predictions (internal forward model).This study proposes a model of cerebellar pathways deduced from both biological and physical constraints. The model learns the dynamic inverse computation of the effect of gravitational torques from its sensorimotor predictions without calculating an explicit inverse computation. By using supervised learning, this model learns to control an anthropomorphic robot arm actuated by two antagonists McKibben artificial muscles. This was achieved by using internal parallel feedback loops containing neural networks which anticipate the sensorimotor consequences of the neural commands. The artificial neural networks architecture was similar to the large-scale connectivity of the cerebellar cortex. Movements in the sagittal plane were performed during three sessions combining different initial positions, amplitudes and directions of movements to vary the effects of the gravitational torques applied to the robotic arm. The results show that this model acquired an internal representation of the gravitational effects during vertical arm pointing movements.This is consistent with the proposal that the cerebellar cortex contains an internal representation of gravitational torques which is encoded through a learning process. Furthermore, this model suggests that the cerebellum performs the inverse dynamics computation based on sensorimotor predictions. This highlights the importance of sensorimotor predictions of gravitational torques acting on upper limb movements performed in the gravitational field

On Neuromechanical Approaches for the Study of Biological Grasp and Manipulation

Biological and robotic grasp and manipulation are undeniably similar at the level of mechanical task performance. However, their underlying fundamental biological vs. engineering mechanisms are, by definition, dramatically different and can even be antithetical. Even our approach to each is diametrically opposite: inductive science for the study of biological systems vs. engineering synthesis for the design and construction of robotic systems. The past 20 years have seen several conceptual advances in both fields and the quest to unify them. Chief among them is the reluctant recognition that their underlying fundamental mechanisms may actually share limited common ground, while exhibiting many fundamental differences. This recognition is particularly liberating because it allows us to resolve and move beyond multiple paradoxes and contradictions that arose from the initial reasonable assumption of a large common ground. Here, we begin by introducing the perspective of neuromechanics, which emphasizes that real-world behavior emerges from the intimate interactions among the physical structure of the system, the mechanical requirements of a task, the feasible neural control actions to produce it, and the ability of the neuromuscular system to adapt through interactions with the environment. This allows us to articulate a succinct overview of a few salient conceptual paradoxes and contradictions regarding under-determined vs. over-determined mechanics, under- vs. over-actuated control, prescribed vs. emergent function, learning vs. implementation vs. adaptation, prescriptive vs. descriptive synergies, and optimal vs. habitual performance. We conclude by presenting open questions and suggesting directions for future research. We hope this frank assessment of the state-of-the-art will encourage and guide these communities to continue to interact and make progress in these important areas

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

abstract: Biological and robotic grasp and manipulation are undeniably similar at the level of mechanical task performance. However, their underlying fundamental biological vs. engineering mechanisms are, by definition, dramatically different and can even be antithetical. Even our approach to each is diametrically opposite: inductive science for the study of biological systems vs. engineering synthesis for the design and construction of robotic systems. The past 20 years have seen several conceptual advances in both fields and the quest to unify them. Chief among them is the reluctant recognition that their underlying fundamental mechanisms may actually share limited common ground, while exhibiting many fundamental differences. This recognition is particularly liberating because it allows us to resolve and move beyond multiple paradoxes and contradictions that arose from the initial reasonable assumption of a large common ground. Here, we begin by introducing the perspective of neuromechanics, which emphasizes that real-world behavior emerges from the intimate interactions among the physical structure of the system, the mechanical requirements of a task, the feasible neural control actions to produce it, and the ability of the neuromuscular system to adapt through interactions with the environment. This allows us to articulate a succinct overview of a few salient conceptual paradoxes and contradictions regarding under-determined vs. over-determined mechanics, under- vs. over-actuated control, prescribed vs. emergent function, learning vs. implementation vs. adaptation, prescriptive vs. descriptive synergies, and optimal vs. habitual performance. We conclude by presenting open questions and suggesting directions for future research. We hope this frank and open-minded assessment of the state-of-the-art will encourage and guide these communities to continue to interact and make progress in these important areas at the interface of neuromechanics, neuroscience, rehabilitation and robotics.The electronic version of this article is the complete one and can be found online at: https://jneuroengrehab.biomedcentral.com/articles/10.1186/s12984-017-0305-

Recurrent cerebellar architecture solves the motor-error problem

Current views of cerebellar function have been heavily influenced by the models of Marr and Albus, who suggested that the climbing fibre input to the cerebellum acts as a teaching signal for motor learning. It is commonly assumed that this teaching signal must be motor error (the difference between actual and correct motor command), but this approach requires complex neural structures to estimate unobservable motor error from its observed sensory consequences. We have proposed elsewhere a recurrent decorrelation control architecture in which Marr-Albus models learn without requiring motor error. Here, we prove convergence for this architecture and demonstrate important advantages for the modular control of systems with multiple degrees of freedom. These results are illustrated by modelling adaptive plant compensation for the three-dimensional vestibular ocular reflex. This provides a functional role for recurrent cerebellar connectivity, which may be a generic anatomical feature of projections between regions of cerebral and cerebellar cortex

Konstrukcija robotske ruke s pneumatskim mišićima kao aktuatorima

Rad se bavi analiziranjem pneumatskih mišića kao aktuatora u pneumatici. Pneumatski mišić vrlo je specifičan aktuator čija je primjena još uvijek rijetka zbog nemogućnosti precizne regulacije. Kako pneumatski mišići posjeduju niz prednosti kao što su mala masa, povoljan omjer mase i sile koju mogu proizvesti, lakoća održavanja i dr., omogućena im je primjena u robotskim sustavima antropoidne strukture odnosno humanoidnim robotima. Sve više se primjenjuju i u industrijskoj automatici, međutim, za rašireniju primjenu nužno je savladavanje njihovih negativnih svojstava kao što je npr. nelinearno dinamičko ponašanje. \Na početku se razmatra princip rada mišića koji je važan za daljnju analizu, posebno kod modeliranja i regulacije. Kada se u mišić dovodi zrak, njegova membrana se širi u radijalnom smjeru, a u isto vrijeme se skuplja u aksijalnom smjeru izazivajući vlačnu silu. Spominje se i McKibbenov mišić koji se može smatrati pretečom današnjih umjetnih pneumatskih mišića. Treće poglavlje bavi se konstrukcijom, a na kraju i izradom jednostavnog pneumatskog manipulatora koji koristi dva pneumatska mišića kao aktuatore, čijim se gibanjem preko prijenosnika gibanja prenosi sila, odnosno moment pomoću kojeg se zakreće rukam anipulatora s ugrađenom prihvatnicom. četvrto poglavlje uključuje stvaranje modela pneumatskog mišića koji je korišten u simulaciji. Da bi model bio donekle zadovoljavajući u obzir su uzeti određeni parametri dobiveni eksperimentalnim putem. Treba naglasiti da model koji bi točno opisivao cijeli ovaj sustav može zahtijevati složen postupak analize i sinteze. U petom poglavlju razmatra se regulacija kuta zakreta poluge manipulatora primjenom P,PID, odnosno PI regulatora. Regulacija je moguća, međutim, zbog oscilatornog ponašanja mišića u radu kao i nedovoljno točnog dinamičkog modela sustava, ona je otežana.Za izvedbu regulacijskih algoritama korišteni su programski paketi Matlab i Simulink, te program Matlab – Real Time Workshop koji omogućuje dobivanje izvršne verzije upravljačkog algoritma direktno iz modela načinjenog u Simulinku a koji se može koristiti na samom realnom procesu. Pri tome se upravljački algoritam izvodi u realnom vremenu. Na taj način se jako skraćuje vrijeme kreiranja i naknadnog editiranja algoritma regulacije

Efficient simulation scheme for spiking neural networks

Nearly all neuronal information processing and inter¬neuronal communication in the brain involves action potentials, or spikes, which drive the short-term synaptic dynamics of neurons, but also their long-term dynamics, via synaptic plasticity. In many brain structures, action potential activity is considered to be sparse. This sparseness of activity has been exploited to reduce the computational cost of large-scale network simulations, through the development of "event-driven" simulation schemes. However, existing event-driven simulations schemes use extremely simplified neuronal models. Here, we design, implement and evaluate critically an event-driven algorithm (EDLUT) that uses pre-calculated lookup tables to characterize synaptic and neuronal dynamics. This approach enables the use of more complex (and realistic) neuronal models or data in representing the neurons, while retaining the advantage of high-speed simulation. We demonstrate the method's application for neurons containing exponential synaptic conductances, thereby implementing shunting inhibition, a phenomenon that is critical to cellular computation. We also introduce an improved two-stage event-queue algorithm, which allows the simulations to scale efficiently to highly-connected networks with arbitrary propagation delays. Finally, the scheme readily accommodates implementation of synaptic plasticity mechanisms that depend upon spike timing, enabling future simulations to explore issues of long-term learning and adaptation in large-scale networks

Sustav za sortiranje pomoću manipulatora pokretanog pneumatskim mišićima

Rad se bavi sustavom za sortiranje pomoću manipulatora pokretanog pneumatskim mišićima. Pneumatski mišić nije tako česti aktuator u pneumatskim sustavima, za razliku od npr. pneumatskog cilindra. To je tako zbog poteškoća koje se javljaju pri regulaciji. Međutim, pneumatski mišići posjeduju niz prednosti kao što su mala masa, povoljan odnos mase i sile koju mogu proizvesti, lakoća održavanja i dr., te sličnost s biološkim mišićima, što ih čini zanimljivim za primjenu u robotskim sustavima antropoidne strukture, odnosno humanoidnim robotima. Sve više se primjenjuju i u industrijskoj automatizaciji, kao pogonski elementi manipulatora koji prenose radne dijelove u procesima montaže i sl. Rad započinje razmatranjem principa rada pneumatskog mišića i njegove tehničke izvedbe. U osnovi je radni princip pneumatskog mišića jednostavan: kada se u mišić dovodi zrak, njegova membrana se u radijalnom smjeru širi, a u isto vrijeme u aksijalnom smjeru skuplja, izazivajući vlačnu silu. \Nadalje, ukratko je dan pregled izvedbi pneumatskih mišića koje se spominju u stručnim literaturama i navedene su najčešće primjene. Treće poglavlje bavi se razvojnim procesom sustava za sortiranje, gdje je koncipirana, konstruirana i izrađena nastavna maketa sustava koji se u osnovi sastoji od manipulatora pokretanog pneumatskim mišićima i tzv. dodavača zajedno sa predmetima za sortiranje. Takvi sustavi primjenjivi su u raznim industrijskim procesima za premještanje predmeta istih oblika koji dolaze npr. ispod hidrauličke preše, ili za jednostavnu montažu istih dijelova. U četvrtom poglavlju izveden je nelinearni matematički model pneumatskog manipulatora gdje su u obzir uzete brojne pretpostavke i parametri dobiveni eksperimentalnim putem. Takav model korišten je simulaciji čiji su rezultati zadovoljavajući. Na kraju, izvedena je regulacija kuta zakreta ruke manipulatora u svrhu premještanja (sortiranja) predmeta upotrebom pneumatske prihvatnice. Regulacija je izvedena pomoću klasičnog linearnog PI regulatora, realiziranog u upravljačkom algoritmu. Gotovo cjelokupna konstrukcijska razrada sustava izvršena je pomoću programskih paketa CATIA-e i AutoCAD-a, a za izvedbu regulacijskih algoritama korišteni su programski paketi Matlab i Simulink, te program Matlab – Real Time Workshop koji omogućuje dobivanje izvršne verzije upravljačkog algoritma direktno iz modela načinjenog u Simulinku, a koji se može koristiti na samom realnom procesu. Pri tome se upravljački algoritam izvodi u realnom vremenu. Ovime se znatno skraćuje vrijeme kreiranja i naknadnog editiranja algoritma regulacije

Hybrid Sensing and Adaptive Control for Direct Brain Actuation of Artificial Limbs

Developing a non-invasive direct brain control of artificial limbs is both challenging and desirable. Such a sensory and control system, if successful, will have a profound impact on the disabled. In this dissertation, we present the design and development of a non-invasive, hybrid sensory system, which uses near-infrared spectroscopy (NIRS) and electroencephalography (EEG) to measure brain activity with simultaneous electromyography (EMG) to provide feedback data in a healthy limb. Through the combination of these sensory techniques, we have successfully trained a control system capable of mapping brain activity onto muscle actuation. The design of a control algorithm capable of automatic reconfiguration to account for changing sensor conditions, selection of an appropriate pre-trained network based on input characteristics, and adaptation to adjust output based on the user\u27s activity are investigated. The selection of an appropriate algorithm and its initial performance using our sensory system are presented and discussed. The sensory and control system are designed for application in artificial limb control for persons who have undergone amputation of an upper-extremity. Actuation of the elbow and wrist are the primary focus of the study, with the intent to expand to forearm torsion and hand grasping in subsequent studies. During the course of the investigation, the additional function of treating phantom limb pain was incorporated into the design, which has also lead to increased sensor resolution requirements

Dynamics of embodied dissociated cortical cultures for the control of hybrid biological robots.

The thesis presents a new paradigm for studying the importance of interactions between an organism and its environment using a combination of biology and technology: embodying cultured cortical neurons via robotics. From this platform, explanations of the emergent neural network properties leading to cognition are sought through detailed electrical observation of neural activity. By growing the networks of neurons and glia over multi-electrode arrays (MEA), which can be used to both stimulate and record the activity of multiple neurons in parallel over months, a long-term real-time 2-way communication with the neural network becomes possible. A better understanding of the processes leading to biological cognition can, in turn, facilitate progress in understanding neural pathologies, designing neural prosthetics, and creating fundamentally different types of artificial cognition. Here, methods were first developed to reliably induce and detect neural plasticity using MEAs. This knowledge was then applied to construct sensory-motor mappings and training algorithms that produced adaptive goal-directed behavior. To paraphrase the results, most any stimulation could induce neural plasticity, while the inclusion of temporal and/or spatial information about neural activity was needed to identify plasticity. Interestingly, the plasticity of action potential propagation in axons was observed. This is a notion counter to the dominant theories of neural plasticity that focus on synaptic efficacies and is suggestive of a vast and novel computational mechanism for learning and memory in the brain. Adaptive goal-directed behavior was achieved by using patterned training stimuli, contingent on behavioral performance, to sculpt the network into behaviorally appropriate functional states: network plasticity was not only induced, but could be customized. Clinically, understanding the relationships between electrical stimulation, neural activity, and the functional expression of neural plasticity could assist neuro-rehabilitation and the design of neuroprosthetics. In a broader context, the networks were also embodied with a robotic drawing machine exhibited in galleries throughout the world. This provided a forum to educate the public and critically discuss neuroscience, robotics, neural interfaces, cybernetics, bio-art, and the ethics of biotechnology.Ph.D.Committee Chair: Steve M. Potter; Committee Member: Eric Schumacher; Committee Member: Robert J. Butera; Committee Member: Stephan P. DeWeerth; Committee Member: Thomas D. DeMars