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

Gesture Recognition and Control for Semi-Autonomous Robotic Assistant Surgeons

The next stage for robotics development is to introduce autonomy and cooperation with human agents in tasks that require high levels of precision and/or that exert considerable physical strain. To guarantee the highest possible safety standards, the best approach is to devise a deterministic automaton that performs identically for each operation. Clearly, such approach inevitably fails to adapt itself to changing environments or different human companions. In a surgical scenario, the highest variability happens for the timing of different actions performed within the same phases. This thesis explores the solutions adopted in pursuing automation in robotic minimally-invasive surgeries (R-MIS) and presents a novel cognitive control architecture that uses a multi-modal neural network trained on a cooperative task performed by human surgeons and produces an action segmentation that provides the required timing for actions while maintaining full phase execution control via a deterministic Supervisory Controller and full execution safety by a velocity-constrained Model-Predictive Controller

Robotic learning of force-based industrial manipulation tasks

Even with the rapid technological advancements, robots are still not the most comfortable machines to work with. Firstly, due to the separation of the robot and human workspace which imposes an additional financial burden. Secondly, due to the significant re-programming cost in case of changing products, especially in Small and Medium-sized Enterprises (SMEs). Therefore, there is a significant need to reduce the programming efforts required to enable robots to perform various tasks while sharing the same space with a human operator. Hence, the robot must be equipped with a cognitive and perceptual capabilities that facilitate human-robot interaction. Humans use their various senses to perform tasks such as vision, smell and taste. One sensethat plays a significant role in human activity is ’touch’ or ’force’. For example, holding a cup of tea, or making fine adjustments while inserting a key requires haptic information to achieve the task successfully. In all these examples, force and torque data are crucial for the successful completion of the activity. Also, this information implicitly conveys data about contact force, object stiffness, and many others. Hence, a deep understanding of the execution of such events can bridge the gap between humans and robots. This thesis is being directed to equip an industrial robot with the ability to deal with force perceptions and then learn force-based tasks using Learning from Demonstration (LfD).To learn force-based tasks using LfD, it is essential to extract task-relevant features from the force information. Then, knowledge must be extracted and encoded form the task-relevant features. Hence, the captured skills can be reproduced in a new scenario. In this thesis, these elements of LfD were achieved using different approaches based on the demonstrated task. In this thesis, four robotics problems were addressed using LfD framework. The first challenge was to filter out robots’ internal forces (irrelevant signals) using data-driven approach. The second robotics challenge was the recognition of the Contact State (CS) during assembly tasks. To tackle this challenge, a symbolic based approach was proposed, in which a force/torque signals; during demonstrated assembly, the task was encoded as a sequence of symbols. The third challenge was to learn a human-robot co-manipulation task based on LfD. In this case, an ensemble machine learning approach was proposed to capture such a skill. The last challenge in this thesis, was to learn an assembly task by demonstration with the presents of parts geometrical variation. Hence, a new learning approach based on Artificial Potential Field (APF) to learn a Peg-in-Hole (PiH) assembly task which includes no-contact and contact phases. To sum up, this thesis focuses on the use of data-driven approaches to learning force based task in an industrial context. Hence, different machine learning approaches were implemented, developed and evaluated in different scenarios. Then, the performance of these approaches was compared with mathematical modelling based approaches.</div

A Person-Centric Design Framework for At-Home Motor Learning in Serious Games

abstract: In motor learning, real-time multi-modal feedback is a critical element in guided training. Serious games have been introduced as a platform for at-home motor training due to their highly interactive and multi-modal nature. This dissertation explores the design of a multimodal environment for at-home training in which an autonomous system observes and guides the user in the place of a live trainer, providing real-time assessment, feedback and difficulty adaptation as the subject masters a motor skill. After an in-depth review of the latest solutions in this field, this dissertation proposes a person-centric approach to the design of this environment, in contrast to the standard techniques implemented in related work, to address many of the limitations of these approaches. The unique advantages and restrictions of this approach are presented in the form of a case study in which a system entitled the "Autonomous Training Assistant" consisting of both hardware and software for guided at-home motor learning is designed and adapted for a specific individual and trainer. In this work, the design of an autonomous motor learning environment is approached from three areas: motor assessment, multimodal feedback, and serious game design. For motor assessment, a 3-dimensional assessment framework is proposed which comprises of 2 spatial (posture, progression) and 1 temporal (pacing) domains of real-time motor assessment. For multimodal feedback, a rod-shaped device called the "Intelligent Stick" is combined with an audio-visual interface to provide feedback to the subject in three domains (audio, visual, haptic). Feedback domains are mapped to modalities and feedback is provided whenever the user's performance deviates from the ideal performance level by an adaptive threshold. Approaches for multi-modal integration and feedback fading are discussed. Finally, a novel approach for stealth adaptation in serious game design is presented. This approach allows serious games to incorporate motor tasks in a more natural way, facilitating self-assessment by the subject. An evaluation of three different stealth adaptation approaches are presented and evaluated using the flow-state ratio metric. The dissertation concludes with directions for future work in the integration of stealth adaptation techniques across the field of exergames.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Towards gestural understanding for intelligent robots

Fritsch JN. Towards gestural understanding for intelligent robots. Bielefeld: Universität Bielefeld; 2012.A strong driving force of scientific progress in the technical sciences is the quest for systems that assist humans in their daily life and make their life easier and more enjoyable. Nowadays smartphones are probably the most typical instances of such systems. Another class of systems that is getting increasing attention are intelligent robots. Instead of offering a smartphone touch screen to select actions, these systems are intended to offer a more natural human-machine interface to their users. Out of the large range of actions performed by humans, gestures performed with the hands play a very important role especially when humans interact with their direct surrounding like, e.g., pointing to an object or manipulating it. Consequently, a robot has to understand such gestures to offer an intuitive interface. Gestural understanding is, therefore, a key capability on the way to intelligent robots. This book deals with vision-based approaches for gestural understanding. Over the past two decades, this has been an intensive field of research which has resulted in a variety of algorithms to analyze human hand motions. Following a categorization of different gesture types and a review of other sensing techniques, the design of vision systems that achieve hand gesture understanding for intelligent robots is analyzed. For each of the individual algorithmic steps – hand detection, hand tracking, and trajectory-based gesture recognition – a separate Chapter introduces common techniques and algorithms and provides example methods. The resulting recognition algorithms are considering gestures in isolation and are often not sufficient for interacting with a robot who can only understand such gestures when incorporating the context like, e.g., what object was pointed at or manipulated. Going beyond a purely trajectory-based gesture recognition by incorporating context is an important prerequisite to achieve gesture understanding and is addressed explicitly in a separate Chapter of this book. Two types of context, user-provided context and situational context, are reviewed and existing approaches to incorporate context for gestural understanding are reviewed. Example approaches for both context types provide a deeper algorithmic insight into this field of research. An overview of recent robots capable of gesture recognition and understanding summarizes the currently realized human-robot interaction quality. The approaches for gesture understanding covered in this book are manually designed while humans learn to recognize gestures automatically during growing up. Promising research targeted at analyzing developmental learning in children in order to mimic this capability in technical systems is highlighted in the last Chapter completing this book as this research direction may be highly influential for creating future gesture understanding systems

An original framework for understanding human actions and body language by using deep neural networks

The evolution of both fields of Computer Vision (CV) and Artificial Neural Networks (ANNs) has allowed the development of efficient automatic systems for the analysis of people's behaviour. By studying hand movements it is possible to recognize gestures, often used by people to communicate information in a non-verbal way. These gestures can also be used to control or interact with devices without physically touching them. In particular, sign language and semaphoric hand gestures are the two foremost areas of interest due to their importance in Human-Human Communication (HHC) and Human-Computer Interaction (HCI), respectively. While the processing of body movements play a key role in the action recognition and affective computing fields. The former is essential to understand how people act in an environment, while the latter tries to interpret people's emotions based on their poses and movements; both are essential tasks in many computer vision applications, including event recognition, and video surveillance. In this Ph.D. thesis, an original framework for understanding Actions and body language is presented. The framework is composed of three main modules: in the first one, a Long Short Term Memory Recurrent Neural Networks (LSTM-RNNs) based method for the Recognition of Sign Language and Semaphoric Hand Gestures is proposed; the second module presents a solution based on 2D skeleton and two-branch stacked LSTM-RNNs for action recognition in video sequences; finally, in the last module, a solution for basic non-acted emotion recognition by using 3D skeleton and Deep Neural Networks (DNNs) is provided. The performances of RNN-LSTMs are explored in depth, due to their ability to model the long term contextual information of temporal sequences, making them suitable for analysing body movements. All the modules were tested by using challenging datasets, well known in the state of the art, showing remarkable results compared to the current literature methods

Investigation of an emotional virtual human modelling method

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.In order to simulate virtual humans more realistically and enable them life-like behaviours, several exploration research on emotion calculation, synthetic perception, and decision making process have been discussed. A series of sub-modules have been designed and simulation results have been presented with discussion. A visual based synthetic perception system has been proposed in this thesis, which allows virtual humans to detect the surrounding virtual environment through a collision-based synthetic vision system. It enables autonomous virtual humans to change their emotion states according to stimuli in real time. The synthetic perception system also allows virtual humans to remember limited information within their own First-in-first-out short-term virtual memory. The new emotion generation method includes a novel hierarchical emotion structure and a group of emotion calculation equations, which enables virtual humans to perform emotionally in real-time according to their internal and external factors. Emotion calculation equations used in this research were derived from psychologic emotion measurements. Virtual humans can utilise the information in virtual memory and emotion calculation equations to generate their own numerical emotion states within the hierarchical emotion structure. Those emotion states are important internal references for virtual humans to adopt appropriate behaviours and also key cues for their decision making. The work introduces a dynamic emotional motion database structure for virtual human modelling. When developing realistic virtual human behaviours, lots of subjects were motion-captured whilst performing emotional motions with or without intent. The captured motions were endowed to virtual characters and implemented in different virtual scenarios to help evoke and verify design ideas, possible consequences of simulation (such as fire evacuation). This work also introduced simple heuristics theory into decision making process in order to make the virtual human’s decision making more like real human. Emotion values are proposed as a group of the key cues for decision making under the simple heuristic structures. A data interface which connects the emotion calculation and the decision making structure together has also been designed for the simulation system