Neural Learning of Stable Dynamical Systems based on Data-Driven Lyapunov Candidates

Neumann K, Lemme A, Steil JJ. Neural Learning of Stable Dynamical Systems based on Data-Driven Lyapunov Candidates. Presented at the Int. Conference Intelligent Robotics and Systems, Tokio

Learning Robot Motions with Stable Dynamical Systems under Diffeomorphic Transformations

Neumann K, Steil JJ. Learning Robot Motions with Stable Dynamical Systems under Diffeomorphic Transformations. Robotics and Autonomous Systems. 2015;70(C):1-15

Neural Learning of Vector Fields for Encoding Stable Dynamical Systems

Lemme A, Reinhart F, Neumann K, Steil JJ. Neural Learning of Vector Fields for Encoding Stable Dynamical Systems. Neurocomputing. 2014;141:3-14

Reliability of Extreme Learning Machines

Neumann K. Reliability of Extreme Learning Machines. Bielefeld: Bielefeld University Library; 2014.The reliable application of machine learning methods becomes increasingly important in challenging engineering domains. In particular, the application of extreme learning machines (ELM) seems promising because of their apparent simplicity and the capability of very efficient processing of large and high-dimensional data sets. However, the ELM paradigm is based on the concept of single hidden-layer neural networks with randomly initialized and fixed input weights and is thus inherently unreliable. This black-box character usually repels engineers from application in potentially safety critical tasks. The problem becomes even more severe since, in principle, only sparse and noisy data sets can be provided in such domains. The goal of this thesis is therefore to equip the ELM approach with the abilities to perform in a reliable manner. This goal is approached in three aspects by enhancing the robustness of ELMs to initializations, make ELMs able to handle slow changes in the environment (i.e. input drifts), and allow the incorporation of continuous constraints derived from prior knowledge. It is shown in several diverse scenarios that the novel ELM approach proposed in this thesis ensures a safe and reliable application while simultaneously sustaining the full modeling power of data-driven methods

Bootstrapping movement primitives from complex trajectories

Lemme A. Bootstrapping movement primitives from complex trajectories. Bielefeld: Bielefeld University; 2014

Task Generalization with Stability Guarantees via Elastic Dynamical System Motion Policies

Dynamical System (DS) based Learning from Demonstration (LfD) allows learning of reactive motion policies with stability and convergence guarantees from a few trajectories. Yet, current DS learning techniques lack the flexibility to generalize to new task instances as they ignore explicit task parameters that inherently change the underlying trajectories. In this work, we propose Elastic-DS, a novel DS learning, and generalization approach that embeds task parameters into the Gaussian Mixture Model (GMM) based Linear Parameter Varying (LPV) DS formulation. Central to our approach is the Elastic-GMM, a GMM constrained to SE(3) task-relevant frames. Given a new task instance/context, the Elastic-GMM is transformed with Laplacian Editing and used to re-estimate the LPV-DS policy. Elastic-DS is compositional in nature and can be used to construct flexible multi-step tasks. We showcase its strength on a myriad of simulated and real-robot experiments while preserving desirable control-theoretic guarantees. Supplementary videos can be found at https://sites.google.com/view/elastic-dsComment: Accepted to CoRL 202

Movement primitives as a robotic tool to interpret trajectories through learning-by-doing

Articulated movements are fundamental in many human and robotic tasks. While humans can learn and generalise arbitrarily long sequences of movements, and particularly can optimise them to fit the constraints and features of their body, robots are often programmed to execute point-to-point precise but fixed patterns. This study proposes a new approach to interpreting and reproducing articulated and complex trajectories as a set of known robot-based primitives. Instead of achieving accurate reproductions, the proposed approach aims at interpreting data in an agent-centred fashion, according to an agent's primitive movements. The method improves the accuracy of a reproduction with an incremental process that seeks first a rough approximation by capturing the most essential features of a demonstrated trajectory. Observing the discrepancy between the demonstrated and reproduced trajectories, the process then proceeds with incremental decompositions and new searches in sub-optimal parts of the trajectory. The aim is to achieve an agent-centred interpretation and progressive learning that fits in the first place the robots' capability, as opposed to a data-centred decomposition analysis. Tests on both geometric and human generated trajectories reveal that the use of own primitives results in remarkable robustness and generalisation properties of the method. In particular, because trajectories are understood and abstracted by means of agent-optimised primitives, the method has two main features: 1) Reproduced trajectories are general and represent an abstraction of the data. 2) The algorithm is capable of reconstructing highly noisy or corrupted data without pre-processing thanks to an implicit and emergent noise suppression and feature detection. This study suggests a novel bio-inspired approach to interpreting, learning and reproducing articulated movements and trajectories. Possible applications include drawing, writing, movement generation, object manipulation, and other tasks where the performance requires human-like interpretation and generalisation capabilities

Reflex syncope : an integrative physiological approach

Síncope, a forma mais comum de perda temporária de consciência é responsável por até 5% das idas aos serviços de emergência e até 3% dos internamentos hospitalares. É um problema médico frequente, com múltiplos gatilhos, incapacitante, potencialmente perigoso e desafiante em termos diagnósticos e terapêuticos. Assim, é necessária uma anamnese detalhada para primeiro estabelecer a natureza da perda de consciência, mas, após o diagnóstico, as medidas terapêuticas existentes são pouco eficazes. Embora a fisiopatologia da síncope vasovagal ainda não tenha sido completamente esclarecida, alguns mecanismos subjacentes foram já desvendados. Em última análise, a síncope depende de uma falha transitória na perfusão cerebral pelo que qualquer factor que afecte a circulação sanguínea cerebral pode determinar a ocorrência de síncope. Assim, o objectivo do presente estudo é caracterizar o impacto hemodinâmico e autonómico nos mecanismos subjacentes à síncope reflexa, para melhorar o diagnóstico, o prognóstico e a qualidade de vida dos doentes e dos seus cuidadores. Para isso, desenhámos e implementámos novas ferramentas matemáticas e computacionais que permitem uma avaliação autonómica e hemodinâmica integrada, de forma a aprofundar a compreensão do seu envolvimento nos mecanismos de síncope reflexa. Além disso, refinando a precisão do diagnóstico, a sensibilidade e a especificidade do teste de mesa de inclinação (“tilt test”), estabelecemos uma ferramenta preditiva do episódio iminente de síncope. Isso permitiu-nos estabelecer alternativas de tratamento eficazes e personalizadas para os doentes refractários às opções convencionais, sob a forma de um programa de treino de ortostatismo (“tilt training”), contribuindo para o aumento da sua qualidade de vida e para a redução dos custos directos e indirectos da sua assistência médica. Assim, num estudo verdadeiramente multidisciplinar envolvendo doentes com síncope reflexa refractária à terapêutica, conseguimos demonstrar uma assincronia funcional das respostas reflexas autonómicas e hemodinâmicas, expressas por um desajuste temporal entre o débito cardíaco e as adaptações de resistência total periférica, uma resposta baroreflexa atrasada e um desequilíbrio incremental do tónus autonómico que, em conjunto, poderão resultar de uma disfunção do sistema nervoso autónomo que se traduz por uma reserva simpática diminuída. Igualmente, desenhámos, testámos e implementámos uma plataforma computacional e respectivo software associado - a plataforma FisioSinal –incluindo novas formas, mais dinâmicas, de avaliação integrada autonómica e hemodinâmica, que levaram ao desenvolvimento de algoritmos preditivos para a estratificação de doentes com síncope. Além disso, na aplicação dessas ferramentas, comprovámos a eficácia de um tratamento não invasivo, não disruptivo e integrado, focado na neuromodulação das variáveis autonómicas e cardiovasculares envolvidas nos mecanismos de síncope. Esta terapêutica complementar levou a um aumento substancial da qualidade de vida dos doentes e à abolição dos eventos sincopais na grande maioria dos doentes envolvidos. Em conclusão, o nosso trabalho contribuiu para preencher a lacuna entre a melhor informação científica disponível e sua aplicação na prática clínica, sustentando-se nos três pilares da medicina translacional: investigação básica, clínica e comunidade.Syncope, the most common form of transient loss of consciousness, accounts for up to 5% of emergency room visits and up to 3% of hospital admissions. It is a frequent medical problem with multiple triggers, potentially dangerous, incapacitating, and challenging to diagnose. Therefore, a detailed clinical history is needed first to establish the nature of the loss of consciousness. However, after diagnosis, the therapeutic measures available are still very poor. Although the exact pathophysiology of vasovagal syncope remains to be clarified, some underlying mechanisms have been unveiled, dependent not only on the cause of syncope but also on age and various other factors that affect clinical presentation. Ultimately, syncope depends on a failure of the circulation to perfuse the brain, so any factor affecting blood circulation may determine syncope occurrence. Thus, the purpose of the present study is to understand the impact of the hemodynamic and autonomic functions on reflex syncope mechanisms to improve patients diagnose, prognosis and general quality of life. Bearing that in mind, we designed and implemented new mathematical and computational tools for autonomic and hemodynamic evaluation, in order to deepen the understanding of their involvement in reflex syncope mechanisms. Furthermore, by refining the diagnostic accuracy, sensitivity and specificity of the head-up tilt-table test, we established a predictive tool for the impending syncopal episode. This allowed us to establish effective and personalised treatment alternatives to patient’s refractory to conventional options, contributing to their increase in the quality of life and a reduction of health care and associated costs. In accordance, in a truly multidisciplinary study involving reflex syncope patients, we were able to show an elemental functional asynchrony of hemodynamic and autonomic reflex responses, expressed through a temporal mismatch between cardiac output and total peripheral resistance adaptations, a deferred baroreflex response and an unbalanced, but incremental, autonomic tone, all contributing to autonomic dysfunction, translated into a decreased sympathetic reserve. Through the design, testing and implementation of a computational platform and the associated software - FisioSinal platform -, we developed novel and dynamic ways of autonomic and hemodynamic evaluation, whose data lead to the development of predictive algorithms for syncope patients’risk stratification. Furthermore, through the application of these tools, we showed the effectiveness of a non-invasive, non-disruptive and integrated treatment, focusing on neuromodulation of the autonomic and cardiovascular variables involved in the syncope mechanisms, leading to a substantial increase of quality of life and the abolishment of syncopal events in a vast majority of the enrolled patients. In conclusion, our work contributed to fill the gap between the best available scientific information and its application in the clinical practice by tackling the three pillars of translational medicine: bench-side, bedside and community

Robust learning algorithms for spiking and rate-based neural networks

Inspired by the remarkable properties of the human brain, the fields of machine learning, computational neuroscience and neuromorphic engineering have achieved significant synergistic progress in the last decade. Powerful neural network models rooted in machine learning have been proposed as models for neuroscience and for applications in neuromorphic engineering. However, the aspect of robustness is often neglected in these models. Both biological and engineered substrates show diverse imperfections that deteriorate the performance of computation models or even prohibit their implementation. This thesis describes three projects aiming at implementing robust learning with local plasticity rules in neural networks. First, we demonstrate the advantages of neuromorphic computations in a pilot study on a prototype chip. Thereby, we quantify the speed and energy consumption of the system compared to a software simulation and show how on-chip learning contributes to the robustness of learning. Second, we present an implementation of spike-based Bayesian inference on accelerated neuromorphic hardware. The model copes, via learning, with the disruptive effects of the imperfect substrate and benefits from the acceleration. Finally, we present a robust model of deep reinforcement learning using local learning rules. It shows how backpropagation combined with neuromodulation could be implemented in a biologically plausible framework. The results contribute to the pursuit of robust and powerful learning networks for biological and neuromorphic substrates