Neuromorphic auditory computing: towards a digital, event-based implementation of the hearing sense for robotics

In this work, it is intended to advance on the development of the neuromorphic audio processing systems in robots through the implementation of an open-source neuromorphic cochlea, event-based models of primary auditory nuclei, and their potential use for real-time robotics applications. First, the main gaps when working with neuromorphic cochleae were identified. Among them, the accessibility and usability of such sensors can be considered as a critical aspect. Silicon cochleae could not be as flexible as desired for some applications. However, FPGA-based sensors can be considered as an alternative for fast prototyping and proof-of-concept applications. Therefore, a software tool was implemented for generating open-source, user-configurable Neuromorphic Auditory Sensor models that can be deployed in any FPGA, removing the aforementioned barriers for the neuromorphic research community. Next, the biological principles of the animals' auditory system were studied with the aim of continuing the development of the Neuromorphic Auditory Sensor. More specifically, the principles of binaural hearing were deeply studied for implementing event-based models to perform real-time sound source localization tasks. Two different approaches were followed to extract inter-aural time differences from event-based auditory signals. On the one hand, a digital, event-based design of the Jeffress model was implemented. On the other hand, a novel digital implementation of the Time Difference Encoder model was designed and implemented on FPGA. Finally, three different robotic platforms were used for evaluating the performance of the proposed real-time neuromorphic audio processing architectures. An audio-guided central pattern generator was used to control a hexapod robot in real-time using spiking neural networks on SpiNNaker. Then, a sensory integration application was implemented combining sound source localization and obstacle avoidance for autonomous robots navigation. Lastly, the Neuromorphic Auditory Sensor was integrated within the iCub robotic platform, being the first time that an event-based cochlea is used in a humanoid robot. Then, the conclusions obtained are presented and new features and improvements are proposed for future works.En este trabajo se pretende avanzar en el desarrollo de los sistemas de procesamiento de audio neuromórficos en robots a través de la implementación de una cóclea neuromórfica de código abierto, modelos basados en eventos de los núcleos auditivos primarios, y su potencial uso para aplicaciones de robótica en tiempo real. En primer lugar, se identificaron los principales problemas a la hora de trabajar con cócleas neuromórficas. Entre ellos, la accesibilidad y usabilidad de dichos sensores puede considerarse un aspecto crítico. Los circuitos integrados analógicos que implementan modelos cocleares pueden no pueden ser tan flexibles como se desea para algunas aplicaciones específicas. Sin embargo, los sensores basados en FPGA pueden considerarse una alternativa para el desarrollo rápido y flexible de prototipos y aplicaciones de prueba de concepto. Por lo tanto, en este trabajo se implementó una herramienta de software para generar modelos de sensores auditivos neuromórficos de código abierto y configurables por el usuario, que pueden desplegarse en cualquier FPGA, eliminando las barreras mencionadas para la comunidad de investigación neuromórfica. A continuación, se estudiaron los principios biológicos del sistema auditivo de los animales con el objetivo de continuar con el desarrollo del Sensor Auditivo Neuromórfico (NAS). Más concretamente, se estudiaron en profundidad los principios de la audición binaural con el fin de implementar modelos basados en eventos para realizar tareas de localización de fuentes sonoras en tiempo real. Se siguieron dos enfoques diferentes para extraer las diferencias temporales interaurales de las señales auditivas basadas en eventos. Por un lado, se implementó un diseño digital basado en eventos del modelo Jeffress. Por otro lado, se diseñó una novedosa implementación digital del modelo de codificador de diferencias temporales y se implementó en FPGA. Por último, se utilizaron tres plataformas robóticas diferentes para evaluar el rendimiento de las arquitecturas de procesamiento de audio neuromórfico en tiempo real propuestas. Se utilizó un generador central de patrones guiado por audio para controlar un robot hexápodo en tiempo real utilizando redes neuronales pulsantes en SpiNNaker. A continuación, se implementó una aplicación de integración sensorial que combina la localización de fuentes de sonido y la evitación de obstáculos para la navegación de robots autónomos. Por último, se integró el Sensor Auditivo Neuromórfico dentro de la plataforma robótica iCub, siendo la primera vez que se utiliza una cóclea basada en eventos en un robot humanoide. Por último, en este trabajo se presentan las conclusiones obtenidas y se proponen nuevas funcionalidades y mejoras para futuros trabajos

Decentralised Compliant Control for Hexapod Robots: A Stick Insect Based Walking Model

Institute of Perception, Action and BehaviourThis thesis aims to transfer knowledge from insect biology into a hexapod walking robot. The similarity of the robot model to the biological target allows the testing of hypotheses regarding control and behavioural strategies in the insect. Therefore, this thesis supports biorobotic research by demonstrating that robotic implementations are improved by using biological strategies and these models can be used to understand biological systems. Specifically, this thesis addresses two central problems in hexapod walking control: the single leg control mechanism and its control variables; and the different roles of the front, middle and hind legs that allow a decentralised architecture to co-ordinate complex behavioural tasks. To investigate these problems, behavioural studies on insect curve walking were combined with quantitative simulations. Behavioural experiments were designed to explore the control of turns of freely walking stick insects, Carausius morosus, toward a visual target. A program for insect tracking and kinematic analysis of observed motion was developed. The results demonstrate that the front legs are responsible for most of the body trajectory. Nonetheless, to replicate insect walking behaviour it is necessary for all legs to contribute with specific roles. Additionally, statistics on leg stepping show that middle and hind legs continuously influence each other. This cannot be explained by previous models that heavily depend on positive feedback controllers. After careful analysis, it was found that the hind legs could actively rotate the body while the middle legs move to the inside of the curve, tangentially to the body axis. The single leg controller is known to be independent from other legs but still capable of mechanical synchronisation. To explain this behaviour positive feedback controllers have been proposed. This mechanism works for the closed kinematic chain problem, but has complications when implemented in a dynamic model. Furthermore, neurophysiological data indicate that legs always respond to disturbances as a negative feedback controller. Additional experimental data presented herein indicates that legs continuously oppose forces created by other legs. This thesis proposes a model that has a velocity positive feedback control modulated via a subordination variable in cascade with a position negative feedback mechanism as the core controller. This allows legs to oppose external and internal forces without compromising inter-leg collaboration for walking. The single leg controller is implemented using a distributed artificial neural network. This network was trained with a wider range of movement to that so far found in the simulation model. The controller implemented with a plausible biologica

From locomotion to cognition: Bridging the gap between reactive and cognitive behavior in a quadruped robot

The cognitivistic paradigm, which states that cognition is a result of computation with symbols that represent the world, has been challenged by many. The opponents have primarily criticized the detachment from direct interaction with the world and pointed to some fundamental problems (for instance the symbol grounding problem). Instead, they emphasized the constitutive role of embodied interaction with the environment. This has motivated the advancement of synthetic methodologies: the phenomenon of interest (cognition) can be studied by building and investigating whole brain-body-environment systems. Our work is centered around a compliant quadruped robot equipped with a multimodal sensory set. In a series of case studies, we investigate the structure of the sensorimotor space that the application of different actions in different environments by the robot brings about. Then, we study how the agent can autonomously abstract the regularities that are induced by the different conditions and use them to improve its behavior. The agent is engaged in path integration, terrain discrimination and gait adaptation, and moving target following tasks. The nature of the tasks forces the robot to leave the ``here-and-now'' time scale of simple reactive stimulus-response behaviors and to learn from its experience, thus creating a ``minimally cognitive'' setting. Solutions to these problems are developed by the agent in a bottom-up fashion. The complete scenarios are then used to illuminate the concepts that are believed to lie at the basis of cognition: sensorimotor contingencies, body schema, and forward internal models. Finally, we discuss how the presented solutions are relevant for applications in robotics, in particular in the area of autonomous model acquisition and adaptation, and, in mobile robots, in dead reckoning and traversability detection

A Bio-inspired architecture for adaptive quadruped locomotion over irregular terrain

Tese de doutoramento Programa Doutoral em Engenharia Electrónica e de ComputadoresThis thesis presents a tentative advancement on walking control of small quadruped and humanoid position controlled robots, addressing the problem of walk generation by combining dynamical systems approach to motor control, insights from neuroethology research on vertebrate motor control and computational neuroscience. Legged locomotion is a complex dynamical process, despite the seemingly easy and natural behavior of the constantly present proficiency of legged animals. Research on locomotion and motor control in vertebrate animals from the last decades has brought to the attention of roboticists, the potential of the nature’s solutions to robot applications. Recent knowledge on the organization of complex motor generation and on mechanics and dynamics of locomotion has been successfully exploited to pursue agile robot locomotion. The work presented on this manuscript is part of an effort on the pursuit in devising a general, model free solution, for the generation of robust and adaptable walking behaviors. It strives to devise a practical solution applicable to real robots, such as the Sony’s quadruped AIBO and Robotis’ DARwIn- OP humanoid. The discussed solutions are inspired on the functional description of the vertebrate neural systems, especially on the concept of Central Pattern Generators (CPGs), their structure and organization, components and sensorimotor interactions. They use a dynamical systems approach for the implementation of the controller, especially on the use of nonlinear oscillators and exploitation of their properties. The main topics of this thesis are divided into three parts. The first part concerns quadruped locomotion, extending a previous CPG solution using nonlinear oscillators, and discussing an organization on three hierarchical levels of abstraction, sharing the purpose and knowledge of other works. It proposes a CPG solution which generates the walking motion for the whole-leg, which is then organized in a network for the production of quadrupedal gaits. The devised solution is able to produce goal-oriented locomotion and navigation as directed through highlevel commands from local planning methods. In this part, active balance on a standing quadruped is also addressed, proposing a method based on dynamical systems approach, exploring the integration of parallel postural mechanisms from several sensory modalities. The solutions are all successfully tested on the quadruped AIBO robot. In the second part, is addressed bipedal walking for humanoid robots. A CPG solution for biped walking based on the concept of motion primitives is proposed, loosely based on the idea of synergistic organization of vertebrate motor control. A set of motion primitives is shown to produce the basis of simple biped walking, and generalizable to goal-oriented walking. Using the proposed CPG, the inclusion of feedback mechanisms is investigated, for modulation and adaptation of walking, through phase transition control according to foot load information. The proposed solution is validated on the humanoid DARwIn-OP, and its application is evaluated within a whole-body control framework. The third part sidesteps a little from the other two topics. It discusses the CPG as having an alternative role to direct motor generation in locomotion, serving instead as a processor of sensory information for a feedback based motor generation. In this work a reflex based walking controller is devised for the compliant quadruped Oncilla robot, to serve as purely feedback based walking generation. The capabilities of the reflex network are shown in simulations, followed by a brief discussion on its limitations, and how they could be improved by the inclusion of a CPG.Esta tese apresenta uma tentativa de avanço no controlo de locomoção para pequenos robôs quadrúpedes e bipedes controlados por posição, endereçando o problema de geração motora através da combinação da abordagem de sistemas dinâmicos para o controlo motor, e perspectivas de investigação neuroetologia no controlo motor vertebrado e neurociência computacional. Andar é um processo dinâmico e complexo, apesar de parecer um comportamento fácil e natural devido à presença constante de animais proficientes em locomoção terrestre. Investigação na área da locomoção e controlo motor em animais vertebrados nas últimas decadas, trouxe à atenção dos roboticistas o potencial das soluções encontradas pela natureza aplicadas a aplicações robóticas. Conhecimento recente relativo à geração de comportamentos motores complexos e da mecânica da locomoção tem sido explorada com sucesso na procura de locomoção ágil na robótica. O trabalho apresentado neste documento é parte de um esforço no desenho de uma solução geral, e independente de modelos, para a geração robusta e adaptável de comportamentos locomotores. O foco é desenhar uma solução prática, aplicável a robôs reais, tal como o quadrúpede Sony AIBO e o humanóide DARwIn-OP. As soluções discutidas são inspiradas na descrição funcional do sistema nervoso vertebrado, especialmente no conceito de Central Pattern Generators (CPGs), a sua estrutura e organização, componentes e interacção sensorimotora. Estas soluções são implementadas usando uma abordagem em sistemas dinâmicos, focandos o uso de osciladores não lineares e a explorando as suas propriedades. Os tópicos principais desta tese estão divididos em três partes. A primeira parte explora o tema de locomoção quadrúpede, expandindo soluções prévias de CPGs usando osciladores não lineares, e discutindo uma organização em três níveis de abstracção, partilhando as ideias de outros trabalhos. Propõe uma solução de CPG que gera os movimentos locomotores para uma perna, que é depois organizado numa rede, para a produção de marcha quadrúpede. A solução concebida é capaz de produzir locomoção e navegação, comandada através de comandos de alto nível, produzidos por métodos de planeamento local. Nesta parte também endereçado o problema da manutenção do equilíbrio num robô quadrúpede parado, propondo um método baseado na abordagem em sistemas dinâmicos, explorando a integração de mecanismos posturais em paralelo, provenientes de várias modalidades sensoriais. As soluções são todas testadas com sucesso no robô quadrupede AIBO. Na segunda parte é endereçado o problema de locomoção bípede. É proposto um CPG baseado no conceito de motion primitives, baseadas na ideia de uma organização sinergética do controlo motor vertebrado. Um conjunto de motion primitives é usado para produzir a base de uma locomoção bípede simples e generalizável para navegação. Esta proposta de CPG é usada para de seguida se investigar a inclusão de mecanismos de feedback para modulação e adaptação da marcha, através do controlo de transições entre fases, de acordo com a informação de carga dos pés. A solução proposta é validada no robô humanóide DARwIn-OP, e a sua aplicação no contexto do framework de whole-body control é também avaliada. A terceira parte desvia um pouco dos outros dois tópicos. Discute o CPG como tendo um papel alternativo ao controlo motor directo, servindo em vez como um processador de informação sensorial para um mecanismo de locomoção puramente em feedback. Neste trabalho é desenhado um controlador baseado em reflexos para a geração da marcha de um quadrúpede compliant. As suas capacidades são demonstradas em simulação, seguidas por uma breve discussão nas suas limitações, e como estas podem ser ultrapassadas pela inclusão de um CPG.The presented work was possible thanks to the support by the Portuguese Science and Technology Foundation through the PhD grant SFRH/BD/62047/2009

Consider the robot - Abstraction of bioinspired leg coordination and its application to a hexapod robot under consideration of technical constraints

Paskarbeit J. Consider the robot - Abstraction of bioinspired leg coordination and its application to a hexapod robot under consideration of technical constraints. Bielefeld: Universität Bielefeld; 2017.To emulate the movement agility and adaptiveness of stick insects in technical systems such as piezo actuators (Szufnarowski et al. 2014) or hexapod robots (Schneider, Cruse et al. 2006), a direct adaptation of bioinspired walking controllers like WALKNET has often been suggested. However, stick insects have very specific features such as adhesive foot pads that allow them to cling to the ground. Typically, robots do not possess such features. Besides, robots tend to be bigger and heavier than their biological models, usually possessing a different mass distribution as well. This leads to different mechanical and functional properties that need to be addressed in control. Based on the model of the stick insect *Carausius morosus*, the six-legged robot HECTOR was developed in this work to test and evaluate bioinspired controllers. The robot's geometrical layout corresponds to that of the stick insect, scaled up by a factor of 20. Moreover, like the stick insect, the robot features an inherent compliance in its joints. This compliance facilitates walking in uneven terrain since small irregularities can be compensated passively without controller intervention. However, the robot differs from the biological model, e.g., in terms of its size, mass, and mass distribution. Also, it does not possess any means to cling to the ground and therefore must maintain static stability to avoid tilting. To evaluate the ability of stick insects to maintain static stability, experimental data (published by Theunissen et al. (2014)) was examined. It can be shown that stick insects do not maintain static stability at all times. Still, due to their adhesive foot pads, they do not tumble. Therefore, a direct replication of the biological walking controller would not be suitable for the control of HECTOR. In a next step, the bioinspired walking controller WALKNET (Cruse, Kindermann, et al. 1998) was evaluated regarding its applicability for the control of HECTOR. For this purpose, different parametrizations of WALKNET were tested in a simulation environment. For forward walking, parameter sets were found that achieve a high, although not permanent stability. Thus, for the control of HECTOR, which requires continuous stability, a more abstract adaption of the bioinspired coordination had to be found. Based on the original coordination concepts of WALKNET, new coordination mechanisms were developed that incorporate the technical requirements (static stability, angular joint limits, torque constraints, etc.). The ability of the resulting controller to generate insect-like gaits is demonstrated for different walking scenarios in simulation. Moreover, locomotion that is unlikely for insects such as backwards and sidewards walking is shown to be feasible using the novel control approach. At the end of this work the applicability of the approach for the control of the real robot is proved in experiments on visual collision avoidance and basic climbing ability

Bio-Inspired Robotics

Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field

Humanoid Robots

For many years, the human being has been trying, in all ways, to recreate the complex mechanisms that form the human body. Such task is extremely complicated and the results are not totally satisfactory. However, with increasing technological advances based on theoretical and experimental researches, man gets, in a way, to copy or to imitate some systems of the human body. These researches not only intended to create humanoid robots, great part of them constituting autonomous systems, but also, in some way, to offer a higher knowledge of the systems that form the human body, objectifying possible applications in the technology of rehabilitation of human beings, gathering in a whole studies related not only to Robotics, but also to Biomechanics, Biomimmetics, Cybernetics, among other areas. This book presents a series of researches inspired by this ideal, carried through by various researchers worldwide, looking for to analyze and to discuss diverse subjects related to humanoid robots. The presented contributions explore aspects about robotic hands, learning, language, vision and locomotion

Adaptive networks for robotics and the emergence of reward anticipatory circuits

Currently the central challenge facing evolutionary robotics is to determine how best to extend the range and complexity of behaviour supported by evolved neural systems. Implicit in the work described in this thesis is the idea that this might best be achieved through devising neural circuits (tractable to evolutionary exploration) that exhibit complementary functional characteristics. We concentrate on two problem domains; locomotion and sequence learning. For locomotion we compare the use of GasNets and other adaptive networks. For sequence learning we introduce a novel connectionist model inspired by the role of dopamine in the basal ganglia (commonly interpreted as a form of reinforcement learning). This connectionist approach relies upon a new neuron model inspired by notions of energy efficient signalling. Two reward adaptive circuit variants were investigated. These were applied respectively to two learning problems; where action sequences are required to take place in a strict order, and secondly, where action sequences are robust to intermediate arbitrary states. We conclude the thesis by proposing a formal model of functional integration, encompassing locomotion and sequence learning, extending ideas proposed by W. Ross Ashby. A general model of the adaptive replicator is presented, incoporating subsystems that are tuned to continuous variation and discrete or conditional events. Comparisons are made with Ross W. Ashby's model of ultrastability and his ideas on adaptive behaviour. This model is intended to support our assertion that, GasNets (and similar networks) and reward adaptive circuits of the type presented here, are intrinsically complementary. In conclusion we present some ideas on how the co-evolution of GasNet and reward adaptive circuits might lead us to significant improvements in the synthesis of agents capable of exhibiting complex adaptive behaviour

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp