Hierarchical Control for Bipedal Locomotion using Central Pattern Generators and Neural Networks

The complexity of bipedal locomotion may be attributed to the difficulty in synchronizing joint movements while at the same time achieving high-level objectives such as walking in a particular direction. Artificial central pattern generators (CPGs) can produce synchronized joint movements and have been used in the past for bipedal locomotion. However, most existing CPG-based approaches do not address the problem of high-level control explicitly. We propose a novel hierarchical control mechanism for bipedal locomotion where an optimized CPG network is used for joint control and a neural network acts as a high-level controller for modulating the CPG network. By separating motion generation from motion modulation, the high-level controller does not need to control individual joints directly but instead can develop to achieve a higher goal using a low-dimensional control signal. The feasibility of the hierarchical controller is demonstrated through simulation experiments using the Neuro-Inspired Companion (NICO) robot. Experimental results demonstrate the controller's ability to function even without the availability of an exact robot model.Comment: In: Proceedings of the Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL-EpiRob), Oslo, Norway, Aug. 19-22, 201

Use of neural oscillators triggered by loading and hip angles to study the activation patterns at the ankle during walking in humans

Spinale Mustergeneratoren (SPG) sind neuronale Netze ohne supraspinales Input, die zyklische Bewegungen steuern. Wir wollten untersuchen, ob sich SPG an die variablen Anforderungen verschiedener Geschwindigkeiten, Störungen und ungewöhnlicher Koordinationsmuster beim Gehen anpassen können. Das SPG-Modell ist ein Oszillator aus zwei Neuronen; eines aktiviert einen Dorsalextensor und das andere einen Plantarflexor. Das Output des Oszillators repräsentiert die jeweilige Muskelaktivierung. Die Modellparameter wurden angepasst, um eine optimale Passung zwischen simulierten und gemessenen elektromyographischen Daten von gesunden Probanden zu erzielen. Eine hohe Korrelation zwischen simulierten und gemessenen Muskelaktivierungen beim normalen Gehen wies darauf hin, dass spinale Kontrolle in Modellen vom Gehen beim Menschen berücksichtigt sollte werden. Unsere experimentellen Ergebnisse zeigen, dass der Soleus vom Rückenmark kontrolliert werden könnte, aber nicht der Tibialis anterior

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

Function Implementation in a Multi-Gate Junctionless FET Structure

Title from PDF of title page, viewed September 18, 2023Dissertation advisor: Mostafizur RahmanVitaIncludes bibliographical references (pages 95-117)Dissertation (Ph.D.)--Department of Computer Science and Electrical Engineering, Department of Physics and Astronomy. University of Missouri--Kansas City, 2023This dissertation explores designing and implementing a multi-gate junctionless field-effect transistor (JLFET) structure and its potential applications beyond conventional devices. The JLFET is a promising alternative to conventional transistors due to its simplified fabrication process and improved electrical characteristics. However, previous research has focused primarily on the device's performance at the individual transistor level, neglecting its potential for implementing complex functions. This dissertation fills this research gap by investigating the function implementation capabilities of the JLFET structure and proposing novel circuit designs based on this technology. The first part of this dissertation presents a comprehensive review of the existing literature on JLFETs, including their fabrication techniques, operating principles, and performance metrics. It highlights the advantages of JLFETs over traditional metal-oxide-semiconductor field-effect transistors (MOSFETs) and discusses the challenges associated with their implementation. Additionally, the review explores the limitations of conventional transistor technologies, emphasizing the need for exploring alternative device architectures. Building upon the theoretical foundation, the dissertation presents a detailed analysis of the multi-gate JLFET structure and its potential for realizing advanced functions. The study explores the impact of different design parameters, such as channel length, gate oxide thickness, and doping profiles, on the device performance. It investigates the trade-offs between power consumption, speed, and noise immunity, and proposes design guidelines for optimizing the function implementation capabilities of the JLFET. To demonstrate the practical applicability of the JLFET structure, this dissertation introduces several novel circuit designs based on this technology. These designs leverage the unique characteristics of the JLFET, such as its steep subthreshold slope and improved on/off current ratio, to implement complex functions efficiently. The proposed circuits include arithmetic units, memory cells, and digital logic gates. Detailed simulations and analyses are conducted to evaluate their performance, power consumption, and scalability. Furthermore, this dissertation explores the potential of the JLFET structure for emerging technologies, such as neuromorphic computing and bioelectronics. It investigates how the JLFET can be employed to realize energy-efficient and biocompatible devices for applications in artificial intelligence and biomedical engineering. The study investigates the compatibility of the JLFET with various materials and substrates, as well as its integration with other functional components. In conclusion, this dissertation contributes to the field of nanoelectronics by providing a comprehensive investigation into the function implementation capabilities of the multi-gate JLFET structure. It highlights the potential of this device beyond its individual transistor performance and proposes novel circuit designs based on this technology. The findings of this research pave the way for the development of advanced electronic systems that are more energy-efficient, faster, and compatible with emerging applications in diverse fields.Introduction -- Literature review -- Crosstalk principle -- Experiment of crosstalk -- Device architecture -- Simulation & results -- Conclusio

Systèmes cognitifs artificiels : du concept au développement de comportements intelligents en robotique autonome

Les travaux présentés dans le cadre de cette habilitation à diriger des recherches s’appuient sur le principe de la robotique développementale et plus particulièrement sur le paradigme de l’énaction. L’idée n’est donc pas de développer un robot intelligent, mais plutôt de concevoir un robot qui soit capable de le devenir. L’originalité du travail présenté dans ce mémoire repose sur le fait que le système cognitif artificiel est décomposé en deux parties distinctes : la première regroupe des processus cognitifs « inconscients » et la deuxième concerne les processus cognitifs « conscients ». Les processus cognitifs inconscients correspondent aux aptitudes (pré-programmées ou apprises) fonctionnant de manière quasi-automatique, alors que les processus cognitifs conscients contribuent au développement et à l’apprentissage de nouvelles aptitudes. La cognition associée au robot est donc le résultat d’un processus de développement par lequel le robot devient progressivement plus habile et acquiert les connaissances lui permettant d’interpréter le monde qui l’entoure.Ce mémoire se décompose en trois grandes parties. La première partie correspond à un curriculum vitae détaillé présentant l’ensemble de mon parcours professionnel. La deuxième partie est consacrée à la présentation plus approfondie de mes activités de recherches qui se sont focalisées sur le développement de systèmes cognitifs artificiels appliqués à la robotique avec des applications dans les domaines de la locomotion bipède, la perception et l’acquisition autonome de connaissances ainsi que les systèmes multi-robots et l’intelligence distribuée. Enfin, la troisième partie est une compilation de quatre articles de revue représentatives de l’ensemble de mes travaux de recherches

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