Development of Cognitive Capabilities in Humanoid Robots

Merged with duplicate record 10026.1/645 on 03.04.2017 by CS (TIS)Building intelligent systems with human level of competence is the ultimate grand challenge for science and technology in general, and especially for the computational intelligence community. Recent theories in autonomous cognitive systems have focused on the close integration (grounding) of communication with perception, categorisation and action. Cognitive systems are essential for integrated multi-platform systems that are capable of sensing and communicating. This thesis presents a cognitive system for a humanoid robot that integrates abilities such as object detection and recognition, which are merged with natural language understanding and refined motor controls. The work includes three studies; (1) the use of generic manipulation of objects using the NMFT algorithm, by successfully testing the extension of the NMFT to control robot behaviour; (2) a study of the development of a robotic simulator; (3) robotic simulation experiments showing that a humanoid robot is able to acquire complex behavioural, cognitive, and linguistic skills through individual and social learning. The robot is able to learn to handle and manipulate objects autonomously, to cooperate with human users, and to adapt its abilities to changes in internal and environmental conditions. The model and the experimental results reported in this thesis, emphasise the importance of embodied cognition, i.e. the humanoid robot's physical interaction between its body and the environment

Intelligent Management of Hierarchical Behaviors Using a NAO Robot as a Vocational Tutor

In order to create an intelligent system which can hold an interview using the NAO robot as an interviewer playing the role of a vocational tutor were classified and categorized twenty behaviors within five personality profiles. Five basic emotions are considered: Anger, boredom, interest, surprise and joy. Selected behaviors are grouped according to these five different emotions. Common behaviors (e.g., movements or body postures) used by the robot (who assumes the role of vocational tutor) during vocational guidance sessions will be based on a theory of personality traits called the "Five Factor Model". In this context, a predefined set of questions will be asked by the robot according to a theoretical model called "Orientation Model" about the person's vocational preferences. Therefore, NAO can react as conveniently as possible during the interview according to the score of the answer given by the person to the question posed and its personality type. Additionally, based on the answers to these questions, it is established a vocational profile and the robot can to emit a recommendation about person vocation. The results obtained show how the intelligent selection of behaviors can be successfully achieved through the proposed approach, making the interaction between a human and a robot friendlier

Cognitive-developmental learning for a humanoid robot : a caregiver's gift

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (p. 319-341).(cont.) which are then applied to developmentally acquire new object representations. The humanoid robot therefore sees the world through the caregiver's eyes. Building an artificial humanoid robot's brain, even at an infant's cognitive level, has been a long quest which still lies only in the realm of our imagination. Our efforts towards such a dimly imaginable task are developed according to two alternate and complementary views: cognitive and developmental.The goal of this work is to build a cognitive system for the humanoid robot, Cog, that exploits human caregivers as catalysts to perceive and learn about actions, objects, scenes, people, and the robot itself. This thesis addresses a broad spectrum of machine learning problems across several categorization levels. Actions by embodied agents are used to automatically generate training data for the learning mechanisms, so that the robot develops categorization autonomously. Taking inspiration from the human brain, a framework of algorithms and methodologies was implemented to emulate different cognitive capabilities on the humanoid robot Cog. This framework is effectively applied to a collection of AI, computer vision, and signal processing problems. Cognitive capabilities of the humanoid robot are developmentally created, starting from infant-like abilities for detecting, segmenting, and recognizing percepts over multiple sensing modalities. Human caregivers provide a helping hand for communicating such information to the robot. This is done by actions that create meaningful events (by changing the world in which the robot is situated) thus inducing the "compliant perception" of objects from these human-robot interactions. Self-exploration of the world extends the robot's knowledge concerning object properties. This thesis argues for enculturating humanoid robots using infant development as a metaphor for building a humanoid robot's cognitive abilities. A human caregiver redesigns a humanoid's brain by teaching the humanoid robot as she would teach a child, using children's learning aids such as books, drawing boards, or other cognitive artifacts. Multi-modal object properties are learned using these tools and inserted into several recognition schemes,by Artur Miguel Do Amaral Arsenio.Ph.D

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

Desenvolvimento de ferramentas de treino para teleoperação háptica de um robô humanóide

Mestrado emEngenharia MecânicaIn robotics, the teleoperation of biped humanoids is one of the most exciting topics. It has the possibility to bypass complex dynamic models with learning demonstration algorithms using human interaction. For this procedure, the Humanoid Project at the University of Aveiro - PHUA, ingrained in the production of a 27 degree-of-freedom full body humanoid platform teleoperated by means of haptic devices. The current project also comprises a robot model that has be imported into the Virtual Robot Experimentation Platform: V-REP. The usage of the simulator allows multiple exercises with greater speed and shorted setup times, when compared to the teleoperation of the real robot, besides providing more safety for the platform and the operator during the tests. By using the simulator, the user can perform tests and make achievements towards the reproduction of human movement with the interaction of two haptic devices providing force feedback to the operator. The performed maneuvers have their kinematic and dynamic data stored for later application in learning by demonstration algorithms. However, the production of more complex and detailed movements requires large amounts of motor skill from the operator. Due to the continuous change of users in the PHUA, an adaptation period is required for the newly arrived operators to develop an a nity with the complex control system. This work is focused on developing methodologies to lower the required time for the training process. Thanks to the versatility of customization provided by V-REP, it was possible to implement interfaces which utilized visual and haptic guidance to enhance the learning capabilities of the operator. A dedicate workstation, new formulations and support tools that control the simulation were developed in order to create a more intuitive control over the humanoid platform. Operators were instructed to reproduce complex 3D movements under several training conditions (with visual and haptic feedback, only haptic feedback, only visual feedback, with guidance tools and without guidance). Performance was measured in terms of speed, drift from intended trajectory, response to the drift and amplitude of the movement. Findings of this study indicate that, with the newly implemented mechanisms, operators are able to gain control over the humanoid platform within a relatively short period of training. Operators subjected to the guidance programs present an even shorter period of training needed, exhibiting high performance in the overall system. These facts support the role of haptic guidance in acquiring kinesthetic memory in high DOFs systems.Em robótica, a teleoperação de robôs bípede humanóides é um dos tópicos mais emocionante. Tem a possibilidade de contornar modelos dinâmicos rígidos, com algoritmos de aprendizagem por demonstração utilizando interação humana. Para este procedimento, o Projeto Humanóide da Universidade de Aveiro - PHUA, empanha-se na produção de uma plataforma humanóide de corpo inteiro teleoperado com dispositivos hapticos. O estado presente do projeto apresenta um robô humanóide com 27 graus de liberdade. O projeto actual apresenta um modelo do robô importado para a Virtual Robot Exper- imentation Platform: V-REP. O uso do simulador permite vários exercícios com maior velocidade e tempos de preparação curtos, quando comparado com a teleoperação do robô real, além de proporcionar mais segurança para a plataforma e do operador durante os ensaios. Ao utilizar o simulador, o utilizador pode realizar testes à reprodução de movimento humano com a interacção de dois dispositivos de meios hápticos que fornecem força de retorno para o operador. As manobras realizadas têm os seus dados cinemáticos e dinâmicos armazenados para posterior aplicação na aprendizagem por algoritmos de demonstração. No entanto, a produção de movimentos mais complexos e detalhados requer grandes quantidades de habilidade motora do operador. Devido à mudança contínua de usuários no PHUA, um período de adaptação é necessário para os operadores recém-chegados a desenvolver uma a nidade com o complexo sistema de controlo. Este trabalho é focado no desenvolvimento de metodologias para diminuir o tempo necessário para o processo de formação dos utilizadores. Graças à versatilidade de personalização fornecidos pela V-REP, foi possível implementar interfaces que utilizaram orientação visual e haptica para melhorar as capacidades de aprendizagem do operador. Uma estação de trabalho, novas formulações e ferramentas de apoio que controlam a simulação foram desenvolvidos a m de criar um controle mais intuitivo sobre a plataforma humanóide. Os operadores foram instruídos a reproduzir movimentos complexos em 3D sob diversas condições de treino (com feedback visual e haptico, apenas feedback haptico, apenas feedback visual, com ferramentas de orientação e sem orientação). O desempenho foi medido em termos de velocidade, a desvio de trajectória pretendida, a resposta à desvio e o tempo gasto para a criação do movimento. Os resultados deste estudo indicam que, com os mecanismos recém-implementadas, os operadores são capazes de ganhar o controlo sobre a plataforma humanóide dentro de um período relativamente curto de treino. Operadores submetidos a programas de orientação apresentam um período ainda mais curto de formação necessária, exibindo alto desempenho no sistema global. Estes fatos justi cam o papel da orientação haptica em adquirir memória cinestésica em sistemas DOFs elevados

Humanoid Robot Soccer Locomotion and Kick Dynamics: Open Loop Walking, Kicking and Morphing into Special Motions on the Nao Robot

Striker speed and accuracy in the RoboCup (SPL) international robot soccer league is becoming increasingly important as the level of play rises. Competition around the ball is now decided in a matter of seconds. Therefore, eliminating any wasted actions or motions is crucial when attempting to kick the ball. It is common to see a discontinuity between walking and kicking where a robot will return to an initial pose in preparation for the kick action. In this thesis we explore the removal of this behaviour by developing a transition gait that morphs the walk directly into the kick back swing pose. The solution presented here is targeted towards the use of the Aldebaran walk for the Nao robot. The solution we develop involves the design of a central pattern generator to allow for controlled steps with realtime accuracy, and a phase locked loop method to synchronise with the Aldebaran walk so that precise step length control can be activated when required. An open loop trajectory mapping approach is taken to the walk that is stabilized statically through the use of a phase varying joint holding torque technique. We also examine the basic princples of open loop walking, focussing on the commonly overlooked frontal plane motion. The act of kicking itself is explored both analytically and empirically, and solutions are provided that are versatile and powerful. Included as an appendix, the broader matter of striker behaviour (process of goal scoring) is reviewed and we present a velocity control algorithm that is very accurate and efficient in terms of speed of execution

A Dynamical System-based Approach to Modeling Stable Robot Control Policies via Imitation Learning

Despite tremendous advances in robotics, we are still amazed by the proficiency with which humans perform movements. Even new waves of robotic systems still rely heavily on hardcoded motions with a limited ability to react autonomously and robustly to a dynamically changing environment. This thesis focuses on providing possible mechanisms to push the level of adaptivity, reactivity, and robustness of robotic systems closer to human movements. Specifically, it aims at developing these mechanisms for a subclass of robot motions called “reaching movements”, i.e. movements in space stopping at a given target (also referred to as episodic motions, discrete motions, or point-to-point motions). These reaching movements can then be used as building blocks to form more advanced robot tasks. To achieve a high level of proficiency as described above, this thesis particularly seeks to derive control policies that: 1) resemble human motions, 2) guarantee the accomplishment of the task (if the target is reachable), and 3) can instantly adapt to changes in dynamic environments. To avoid manually hardcoding robot motions, this thesis exploits the power of machine learning techniques and takes an Imitation Learning (IL) approach to build a generic model of robot movements from a few examples provided by an expert. To achieve the required level of robustness and reactivity, the perspective adopted in this thesis is that a reaching movement can be described with a nonlinear Dynamical System (DS). When building an estimate of DS from demonstrations, there are two key problems that need to be addressed: the problem of generating motions that resemble at best the demonstrations (the “how-to-imitate” problem), and most importantly, the problem of ensuring the accomplishment of the task, i.e. reaching the target (the “stability” problem). Although there are numerous well-established approaches in robotics that could answer each of these problems separately, tackling both problems simultaneously is challenging and has not been extensively studied yet. This thesis first tackles the problem mentioned above by introducing an iterative method to build an estimate of autonomous nonlinear DS that are formulated as a mixture of Gaussian functions. This method minimizes the number of Gaussian functions required for achieving both local asymptotic stability at the target and accuracy in following demonstrations. We then extend this formulation and provide sufficient conditions to ensure global asymptotic stability of autonomous DS at the target. In this approach, an estimation of the underlying DS is built by solving a constraint optimization problem, where the metric of accuracy and the stability conditions are formulated as the optimization objective and constraints, respectively. In addition to ensuring convergence of all motions to the target within the local or global stability regions, these approaches offer an inherent adaptability and robustness to changes in dynamic environments. This thesis further extends the previous approaches and ensures global asymptotic stability of DS-based motions at the target independently of the choice of the regression technique. Therefore, it offers the possibility to choose the most appropriate regression technique based on the requirements of the task at hand without compromising DS stability. This approach also provides the possibility of online learning and using a combination of two or more regression methods to model more advanced robot tasks, and can be applied to estimate motions that are represented with both autonomous and non-autonomous DS. Additionally, this thesis suggests a reformulation to modeling robot motions that allows encoding of a considerably wider set of tasks ranging from reaching movements to agile robot movements that require hitting a given target with a specific speed and direction. This approach is validated in the context of playing the challenging task of minigolf. Finally, the last part of this thesis proposes a DS-based approach to realtime obstacle avoidance. The presented approach provides a modulation that instantly modifies the robot’s motion to avoid collision with multiple static and moving convex obstacles. This approach can be applied on all the techniques described above without affecting their adaptability, swiftness, or robustness. The techniques that are developed in this thesis have been validated in simulation and on different robotic platforms including the humanoid robots HOAP-3 and iCub, and the robot arms KATANA, WAM, and LWR. Throughout this thesis we show that the DS-based approach to modeling robot discrete movements can offer a high level of adaptability, reactivity, and robustness almost effortlessly when interacting with dynamic environments

Visuomotor Coordination in Reach-To-Grasp Tasks: From Humans to Humanoids and Vice Versa

Understanding the principles involved in visually-based coordinated motor control is one of the most fundamental and most intriguing research problems across a number of areas, including psychology, neuroscience, computer vision and robotics. Not very much is known regarding computational functions that the central nervous system performs in order to provide a set of requirements for visually-driven reaching and grasping. Additionally, in spite of several decades of advances in the field, the abilities of humanoids to perform similar tasks are by far modest when needed to operate in unstructured and dynamically changing environments. More specifically, our first focus is understanding the principles involved in human visuomotor coordination. Not many behavioral studies considered visuomotor coordination in natural, unrestricted, head-free movements in complex scenarios such as obstacle avoidance. To fill this gap, we provide an assessment of visuomotor coordination when humans perform prehensile tasks with obstacle avoidance, an issue that has received far less attention. Namely, we quantify the relationships between the gaze and arm-hand systems, so as to inform robotic models, and we investigate how the presence of an obstacle modulates this pattern of correlations. Second, to complement these observations, we provide a robotic model of visuomotor coordination, with and without the presence of obstacles in the workspace. The parameters of the controller are solely estimated by using the human motion capture data from our human study. This controller has a number of interesting properties. It provides an efficient way to control the gaze, arm and hand movements in a stable and coordinated manner. When facing perturbations while reaching and grasping, our controller adapts its behavior almost instantly, while preserving coordination between the gaze, arm, and hand. In the third part of the thesis, we study the neuroscientific literature of the primates. We here stress the view that the cerebellum uses the cortical reference frame representation. The cerebellum by taking into account this representation performs closed-loop programming of multi-joint movements and movement synchronization between the eye-head system, arm and hand. Based on this investigation, we propose a functional architecture of the cerebellar-cortical involvement. We derive a number of improvements of our visuomotor controller for obstacle-free reaching and grasping. Because this model is devised by carefully taking into account the neuroscientific evidence, we are able to provide a number of testable predictions about the functions of the central nervous system in visuomotor coordination. Finally, we tackle the flow of the visuomotor coordination in the direction from the arm-hand system to the visual system. We develop two models of motor-primed attention for humanoid robots. Motor-priming of attention is a mechanism that implements prioritizing of visual processing with respect to motor-relevant parts of the visual field. Recent studies in humans and monkeys have shown that visual attention supporting natural behavior is not exclusively defined in terms of visual saliency in color or texture cues, rather the reachable space and motor plans present the predominant source of this attentional modulation. Here, we show that motor-priming of visual attention can be used to efficiently distribute robot's computational resources devoted to visual processing