Motion representation with spiking neural networks for grasping and manipulation

Die Natur bedient sich Millionen von Jahren der Evolution, um adaptive physikalische Systeme mit effizienten Steuerungsstrategien zu erzeugen. Im Gegensatz zur konventionellen Robotik plant der Mensch nicht einfach eine Bewegung und führt sie aus, sondern es gibt eine Kombination aus mehreren Regelkreisen, die zusammenarbeiten, um den Arm zu bewegen und ein Objekt mit der Hand zu greifen. Mit der Forschung an humanoiden und biologisch inspirierten Robotern werden komplexe kinematische Strukturen und komplizierte Aktor- und Sensorsysteme entwickelt. Diese Systeme sind schwierig zu steuern und zu programmieren, und die klassischen Methoden der Robotik können deren Stärken nicht immer optimal ausnutzen. Die neurowissenschaftliche Forschung hat große Fortschritte beim Verständnis der verschiedenen Gehirnregionen und ihrer entsprechenden Funktionen gemacht. Dennoch basieren die meisten Modelle auf groß angelegten Simulationen, die sich auf die Reproduktion der Konnektivität und der statistischen neuronalen Aktivität konzentrieren. Dies öffnet eine Lücke bei der Anwendung verschiedener Paradigmen, um Gehirnmechanismen und Lernprinzipien zu validieren und Funktionsmodelle zur Steuerung von Robotern zu entwickeln. Ein vielversprechendes Paradigma ist die ereignis-basierte Berechnung mit SNNs. SNNs fokussieren sich auf die biologischen Aspekte von Neuronen und replizieren deren Arbeitsweise. Sie sind für spike- basierte Kommunikation ausgelegt und ermöglichen die Erforschung von Mechanismen des Gehirns für das Lernen mittels neuronaler Plastizität. Spike-basierte Kommunikation nutzt hoch parallelisierten Hardware-Optimierungen mittels neuromorpher Chips, die einen geringen Energieverbrauch und schnelle lokale Operationen ermöglichen. In dieser Arbeit werden verschiedene SNNs zur Durchführung von Bewegungss- teuerung für Manipulations- und Greifaufgaben mit einem Roboterarm und einer anthropomorphen Hand vorgestellt. Diese basieren auf biologisch inspirierten funktionalen Modellen des menschlichen Gehirns. Ein Motor-Primitiv wird auf parametrische Weise mit einem Aktivierungsparameter und einer Abbildungsfunktion auf die Roboterkinematik übertragen. Die Topologie des SNNs spiegelt die kinematische Struktur des Roboters wider. Die Steuerung des Roboters erfolgt über das Joint Position Interface. Um komplexe Bewegungen und Verhaltensweisen modellieren zu können, werden die Primitive in verschiedenen Schichten einer Hierarchie angeordnet. Dies ermöglicht die Kombination und Parametrisierung der Primitiven und die Wiederverwendung von einfachen Primitiven für verschiedene Bewegungen. Es gibt verschiedene Aktivierungsmechanismen für den Parameter, der ein Motorprimitiv steuert — willkürliche, rhythmische und reflexartige. Außerdem bestehen verschiedene Möglichkeiten neue Motorprimitive entweder online oder offline zu lernen. Die Bewegung kann entweder als Funktion modelliert oder durch Imitation der menschlichen Ausführung gelernt werden. Die SNNs können in andere Steuerungssysteme integriert oder mit anderen SNNs kombiniert werden. Die Berechnung der inversen Kinematik oder die Validierung von Konfigurationen für die Planung ist nicht erforderlich, da der Motorprimitivraum nur durchführbare Bewegungen hat und keine ungültigen Konfigurationen enthält. Für die Evaluierung wurden folgende Szenarien betrachtet, das Zeigen auf verschiedene Ziele, das Verfolgen einer Trajektorie, das Ausführen von rhythmischen oder sich wiederholenden Bewegungen, das Ausführen von Reflexen und das Greifen von einfachen Objekten. Zusätzlich werden die Modelle des Arms und der Hand kombiniert und erweitert, um die mehrbeinige Fortbewegung als Anwendungsfall der Steuerungsarchitektur mit Motorprimitiven zu modellieren. Als Anwendungen für einen Arm (3 DoFs) wurden die Erzeugung von Zeigebewegungen und das perzeptionsgetriebene Erreichen von Zielen modelliert. Zur Erzeugung von Zeigebewegun- gen wurde ein Basisprimitiv, das auf den Mittelpunkt einer Ebene zeigt, offline mit vier Korrekturprimitiven kombiniert, die eine neue Trajektorie erzeugen. Für das wahrnehmungsgesteuerte Erreichen eines Ziels werden drei Primitive online kombiniert unter Verwendung eines Zielsignals. Als Anwendungen für eine Fünf-Finger-Hand (9 DoFs) wurden individuelle Finger-aktivierungen und Soft-Grasping mit nachgiebiger Steuerung modelliert. Die Greif- bewegungen werden mit Motor-Primitiven in einer Hierarchie modelliert, wobei die Finger-Primitive die Synergien zwischen den Gelenken und die Hand-Primitive die unterschiedlichen Affordanzen zur Koordination der Finger darstellen. Für jeden Finger werden zwei Reflexe hinzugefügt, zum Aktivieren oder Stoppen der Bewegung bei Kontakt und zum Aktivieren der nachgiebigen Steuerung. Dieser Ansatz bietet enorme Flexibilität, da Motorprimitive wiederverwendet, parametrisiert und auf unterschiedliche Weise kombiniert werden können. Neue Primitive können definiert oder gelernt werden. Ein wichtiger Aspekt dieser Arbeit ist, dass im Gegensatz zu Deep Learning und End-to-End-Lernmethoden, keine umfangreichen Datensätze benötigt werden, um neue Bewegungen zu lernen. Durch die Verwendung von Motorprimitiven kann der gleiche Modellierungsansatz für verschiedene Roboter verwendet werden, indem die Abbildung der Primitive auf die Roboterkinematik neu definiert wird. Die Experimente zeigen, dass durch Motor- primitive die Motorsteuerung für die Manipulation, das Greifen und die Lokomotion vereinfacht werden kann. SNNs für Robotikanwendungen ist immer noch ein Diskussionspunkt. Es gibt keinen State-of-the-Art-Lernalgorithmus, es gibt kein Framework ähnlich dem für Deep Learning, und die Parametrisierung von SNNs ist eine Kunst. Nichtsdestotrotz können Robotikanwendungen - wie Manipulation und Greifen - Benchmarks und realistische Szenarien liefern, um neurowissenschaftliche Modelle zu validieren. Außerdem kann die Robotik die Möglichkeiten der ereignis- basierten Berechnung mit SNNs und neuromorpher Hardware nutzen. Die physikalis- che Nachbildung eines biologischen Systems, das vollständig mit SNNs implementiert und auf echten Robotern evaluiert wurde, kann neue Erkenntnisse darüber liefern, wie der Mensch die Motorsteuerung und Sensorverarbeitung durchführt und wie diese in der Robotik angewendet werden können. Modellfreie Bewegungssteuerungen, inspiriert von den Mechanismen des menschlichen Gehirns, können die Programmierung von Robotern verbessern, indem sie die Steuerung adaptiver und flexibler machen

Are We the Robots? : Man-Machine Integration

We experience and interact with the world through our body. The founding father of computer science, Alan Turing, correctly realized that one of the most important features of the human being is the interaction between mind and body. Since the original demonstration that electrical activity of the cortical neurons can be employed to directly control a robotic device, the research on the so-called Brain-Machine Interfaces (BMIs) has impressively grown. For example, current BMIs dedicated to both experimental and clinical studies can translate raw neuronal signals into computational commands to reproduce reaching or grasping in artificial actuators. These developments hold promise for the restoration of limb mobility in paralyzed individuals. However, as the authors review in this chapter, before this goal can be achieved, several hurdles have to be overcome, including developments in real-time computational algorithms and in designing fully implantable and biocompatible devices. Future investigations will have to address the best solutions for restoring sensation to the prosthetic limb, which still remains a major challenge to full integration of the limb into the user's self-image

Evolution of Grasping Behaviour in Anthropomorphic Robotic Arms with Embodied Neural Controllers

The works reported in this thesis focus upon synthesising neural controllers for anthropomorphic robots that are able to manipulate objects through an automatic design process based on artificial evolution. The use of Evolutionary Robotics makes it possible to reduce the characteristics and parameters specified by the designer to a minimum, and the robot’s skills evolve as it interacts with the environment. The primary objective of these experiments is to investigate whether neural controllers that are regulating the state of the motors on the basis of the current and previously experienced sensors (i.e. without relying on an inverse model) can enable the robots to solve such complex tasks. Another objective of these experiments is to investigate whether the Evolutionary Robotics approach can be successfully applied to scenarios that are significantly more complex than those to which it is typically applied (in terms of the complexity of the robot’s morphology, the size of the neural controller, and the complexity of the task). The obtained results indicate that skills such as reaching, grasping, and discriminating among objects can be accomplished without the need to learn precise inverse internal models of the arm/hand structure. This would also support the hypothesis that the human central nervous system (cns) does necessarily have internal models of the limbs (not excluding the fact that it might possess such models for other purposes), but can act by shifting the equilibrium points/cycles of the underlying musculoskeletal system. Consequently, the resulting controllers of such fundamental skills would be less complex. Thus, the learning of more complex behaviours will be easier to design because the underlying controller of the arm/hand structure is less complex. Moreover, the obtained results also show how evolved robots exploit sensory-motor coordination in order to accomplish their tasks

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

A Posture Sequence Learning System for an Anthropomorphic Robotic Hand

The paper presents a cognitive architecture for posture learning of an anthropomorphic robotic hand. Our approach is aimed to allow the robotic system to perform complex perceptual operations, to interact with a human user and to integrate the perceptions by a cognitive representation of the scene and the observed actions. The anthropomorphic robotic hand imitates the gestures acquired by the vision system in order to learn meaningful movements, to build its knowledge by different conceptual spaces and to perform complex interaction with the human operator

Muscleless Motor synergies and actions without movements : From Motor neuroscience to cognitive robotics

Emerging trends in neurosciences are providing converging evidence that cortical networks in predominantly motor areas are activated in several contexts related to ‘action’ that do not cause any overt movement. Indeed for any complex body, human or embodied robot inhabiting unstructured environments, the dual processes of shaping motor output during action execution and providing the self with information related to feasibility, consequence and understanding of potential actions (of oneself/others) must seamlessly alternate during goal-oriented behaviors, social interactions. While prominent approaches like Optimal Control, Active Inference converge on the role of forward models, they diverge on the underlying computational basis. In this context, revisiting older ideas from motor control like the Equilibrium Point Hypothesis and synergy formation, this article offers an alternative perspective emphasizing the functional role of a ‘plastic, configurable’ internal representation of the body (body-schema) as a critical link enabling the seamless continuum between motor control and imagery. With the central proposition that both “real and imagined” actions are consequences of an internal simulation process achieved though passive goal-oriented animation of the body schema, the computational/neural basis of muscleless motor synergies (and ensuing simulated actions without movements) is explored. The rationale behind this perspective is articulated in the context of several interdisciplinary studies in motor neurosciences (for example, intracranial depth recordings from the parietal cortex, FMRI studies highlighting a shared cortical basis for action ‘execution, imagination and understanding’), animal cognition (in particular, tool-use and neuro-rehabilitation experiments, revealing how coordinated tools are incorporated as an extension to the body schema) and pertinent challenges towards building cognitive robots that can seamlessly “act, interact, anticipate and understand” in unstructured natural living spaces

Probabilistic Models of Motor Production

N. Bernstein defined the ability of the central neural system (CNS) to control many degrees of freedom of a physical body with all its redundancy and flexibility as the main problem in motor control. He pointed at that man-made mechanisms usually have one, sometimes two degrees of freedom (DOF); when the number of DOF increases further, it becomes prohibitively hard to control them. The brain, however, seems to perform such control effortlessly. He suggested the way the brain might deal with it: when a motor skill is being acquired, the brain artificially limits the degrees of freedoms, leaving only one or two. As the skill level increases, the brain gradually "frees" the previously fixed DOF, applying control when needed and in directions which have to be corrected, eventually arriving to the control scheme where all the DOF are "free". This approach of reducing the dimensionality of motor control remains relevant even today. One the possibles solutions of the Bernstetin's problem is the hypothesis of motor primitives (MPs) - small building blocks that constitute complex movements and facilitite motor learnirng and task completion. Just like in the visual system, having a homogenious hierarchical architecture built of similar computational elements may be beneficial. Studying such a complicated object as brain, it is important to define at which level of details one works and which questions one aims to answer. David Marr suggested three levels of analysis: 1. computational, analysing which problem the system solves; 2. algorithmic, questioning which representation the system uses and which computations it performs; 3. implementational, finding how such computations are performed by neurons in the brain. In this thesis we stay at the first two levels, seeking for the basic representation of motor output. In this work we present a new model of motor primitives that comprises multiple interacting latent dynamical systems, and give it a full Bayesian treatment. Modelling within the Bayesian framework, in my opinion, must become the new standard in hypothesis testing in neuroscience. Only the Bayesian framework gives us guarantees when dealing with the inevitable plethora of hidden variables and uncertainty. The special type of coupling of dynamical systems we proposed, based on the Product of Experts, has many natural interpretations in the Bayesian framework. If the dynamical systems run in parallel, it yields Bayesian cue integration. If they are organized hierarchically due to serial coupling, we get hierarchical priors over the dynamics. If one of the dynamical systems represents sensory state, we arrive to the sensory-motor primitives. The compact representation that follows from the variational treatment allows learning of a motor primitives library. Learned separately, combined motion can be represented as a matrix of coupling values. We performed a set of experiments to compare different models of motor primitives. In a series of 2-alternative forced choice (2AFC) experiments participants were discriminating natural and synthesised movements, thus running a graphics Turing test. When available, Bayesian model score predicted the naturalness of the perceived movements. For simple movements, like walking, Bayesian model comparison and psychophysics tests indicate that one dynamical system is sufficient to describe the data. For more complex movements, like walking and waving, motion can be better represented as a set of coupled dynamical systems. We also experimentally confirmed that Bayesian treatment of model learning on motion data is superior to the simple point estimate of latent parameters. Experiments with non-periodic movements show that they do not benefit from more complex latent dynamics, despite having high kinematic complexity. By having a fully Bayesian models, we could quantitatively disentangle the influence of motion dynamics and pose on the perception of naturalness. We confirmed that rich and correct dynamics is more important than the kinematic representation. There are numerous further directions of research. In the models we devised, for multiple parts, even though the latent dynamics was factorized on a set of interacting systems, the kinematic parts were completely independent. Thus, interaction between the kinematic parts could be mediated only by the latent dynamics interactions. A more flexible model would allow a dense interaction on the kinematic level too. Another important problem relates to the representation of time in Markov chains. Discrete time Markov chains form an approximation to continuous dynamics. As time step is assumed to be fixed, we face with the problem of time step selection. Time is also not a explicit parameter in Markov chains. This also prohibits explicit optimization of time as parameter and reasoning (inference) about it. For example, in optimal control boundary conditions are usually set at exact time points, which is not an ecological scenario, where time is usually a parameter of optimization. Making time an explicit parameter in dynamics may alleviate this