The work accomplished in this thesis concerns development of an autonomous machine cognition system. The proposed solution reposes on the assumption that it is the curiosity which motivates a cognitive system to acquire new knowledge. Further, two distinct kinds of curiosity are identified in conformity to human cognitive system. On this I build a two level cognitive architecture. I identify its lower level with the perceptual saliency mechanism, while the higher level performs knowledge acquisition from observation and interaction with the environment. This thesis brings the following contribution: A) Investigation of the state of the art in autonomous knowledge acquisition. B) Realization of a lower cognitive level in the ensemble of the mentioned system, which is realizing the perceptual curiosity mechanism through a novel fast, real-world robust algorithm for salient object detection and learning. C) Realization of a higher cognitive level through a general framework for knowledge acquisition from observation and interaction with the environment including humans. Based on the epistemic curiosity, the high-level cognitive system enables a machine (e.g. a robot) to be itself the actor of its learning. An important consequence of this system is the possibility to confer high level multimodal cognitive capabilities to robots to increase their autonomy in real-world environment (human environment). D) Realization of the strategy proposed in the context of autonomous robotics. The studies and experimental validations done had confirmed notably that our approach allows increasing the autonomy of robots in real-world environment

Le travail effectué lors de cette thèse concerne le développement d'un système cognitif artificiel autonome. La solution proposée repose sur l'hypothèse que la curiosité est une source de motivation d'un système cognitif dans le processus d'acquisition des nouvelles connaissances. En outre, deux types distincts de curiosité ont été identifiés conformément au système cognitif humain. Sur ce principe, une architecture cognitive à deux niveaux a été proposée. Le bas-niveau repose sur le principe de la saillance perceptive, tandis que le haut-niveau réalise l'acquisition des connaissances par l'observation et l'interaction avec l'environnement. Cette thèse apporte les contributions suivantes : A) Un état de l'art sur l'acquisition autonome de connaissance. B) L'étude, la conception et la réalisation d'un système cognitif bas-niveau basé sur le principe de la curiosité perceptive. L'approche proposée repose sur la saillance visuelle réalisée grâce au développement d'un algorithme rapide et robuste permettant la détection et l'apprentissage d'objets saillants. C) La conception d'un système cognitif haut-niveau, basé sur une approche générique, permettant l'acquisition de connaissance à partir de l'observation et de l'interaction avec son environnent (y compris avec les êtres humains). Basé sur la curiosité épistémique, le système cognitif haut-niveau développé permet à une machine (par exemple un robot) de devenir l'acteur de son propre apprentissage. Une conséquence substantielle d'un tel système est la possibilité de conférer des capacités cognitives haut-niveau multimodales à des robots pour accroître leur autonomie dans un environnement réel (environnement humain). D) La mise en œuvre de la stratégie proposée dans le cadre de la robotique autonome. Les études et les validations expérimentales réalisées ont notamment confirmé que notre approche permet d'accroître l'autonomie des robots dans un environnement réel / The work accomplished in this thesis concerns development of an autonomous machine cognition system. The proposed solution reposes on the assumption that it is the curiosity which motivates a cognitive system to acquire new knowledge. Further, two distinct kinds of curiosity are identified in conformity to human cognitive system. On this I build a two level cognitive architecture. I identify its lower level with the perceptual saliency mechanism, while the higher level performs knowledge acquisition from observation and interaction with the environment. This thesis brings the following contribution: A) Investigation of the state of the art in autonomous knowledge acquisition. B) Realization of a lower cognitive level in the ensemble of the mentioned system, which is realizing the perceptual curiosity mechanism through a novel fast, real-world robust algorithm for salient object detection and learning. C) Realization of a higher cognitive level through a general framework for knowledge acquisition from observation and interaction with the environment including humans. Based on the epistemic curiosity, the high-level cognitive system enables a machine (e.g. a robot) to be itself the actor of its learning. An important consequence of this system is the possibility to confer high level multimodal cognitive capabilities to robots to increase their autonomy in real-world environment (human environment). D) Realization of the strategy proposed in the context of autonomous robotics. The studies and experimental validations done had confirmed notably that our approach allows increasing the autonomy of robots in real-world environment

A robótica social representa um ramo da interação humano-robô que visa desenvolver robôs para atuar em ambientes não estruturados em parceria direta com seres humanos. O relatório A Roadmap for U.S. Robotics From Internet to Robotics, de 2013, preconiza a obtenção de resultados promissores em 12 anos desde que condições apropriadas sejam disponibilizadas para a área. Uma das condições envolve a utilização de ambiente de referência para desenvolver, avaliar e comparar o desempenho de sistemas cognitivos. Este ambiente é denominado Robot City com atores, cenários (casas, ruas, cidade) e auditores. Até o momento esse complexo ambiente não se concretizou, possivelmente devido ao elevado custo de implantação e manutenção de uma instalação desse porte. Nesta dissertação é proposto um caminho alternativo através da definição e implementação do simulador de sistemas cognitivos denominado Robot House Simulator (RHS). O simulador RHS tem como objetivo disponibilizar um ambiente residencial composto por sala e cozinha, no qual convivem dois agentes, um robô humanoide e um avatar humano. O agente humano é controlado pelo usuário do sistema e o robô é controlado por uma arquitetura cognitiva que determina o comportamento do robô. A arquitetura cognitiva estabelece sua percepção do ambiente através de informações sensoriais supridas pelo RHS e modeladas por uma ontologia denominada OntSense. A utilização de uma ontologia garante rigidez formal aos dados sensoriais além de viabilizar um alto nivel de abstração. O RHS tem como base a ferramenta de desenvolvimento de jogos Unity sendo aderente ao conceito de código aberto com disponibilização pelo repositório online GitHub. A validação do sistema foi realizada através de experimentos que demonstraram a capacidade do simulador em prover um ambiente de validação para arquiteturas cognitivas voltadas à robótica social. O RHS é pioneiro na integração de um simulador e uma arquitetura cognitiva, além disto, é um dos poucos direcionados para robótica social provendo rica informação sensorial, destacando-se o modelamento inédito disponibilizado para os sentidos de olfato e paladar. / Social robotics represents a branch of human-robot interaction that aims to develop robots to work in unstructured environments in direct partnership with humans. The Roadmap for Robotics from the Internet to Robotics, 2013, predicts achieving promising results in 12 years as long as appropriate conditions are made available to the area. One of the conditions involves the use of a reference environment to develop, evaluate and compare the performance of cognitive systems. This environment is called Robot City with actors, scenarios (houses, streets, city) and auditors. To date, this complex environment has not been materialized, possibly due to its high cost of installing and maintaining. In this dissertation an alternative way is proposed through the definition and implementation of the simulator of cognitive systems called Robot House Simulator (RHS). The RHS simulator aims to provide a residential environment composed of living room and kitchen, in which two agents live together, a humanoid robot and a human avatar. The human avatar is controlled by the user of the system and the robot is controlled by a cognitive architecture that determines the behavior of the robot. The cognitive architecture establishes its perception of the environment through sensorial information supplied by the RHS and modeled by an ontology called OntSense. The use of an ontology guarantees formal rigidity to the sensory data in addition to enabling a high level of abstraction. The RHS simulator is based on the Unity game engine and is adheres to the open source concept, available on the GitHub online repository. The validation of the system was performed through experiments that demonstrated the simulators ability to provide a validation environment for cognitive architectures aimed at social robotics. The RHS simulator is a pioneer in the integration of a simulator and a cognitive architecture. In addition, it is one of the few for social robotics to provide rich sensory information where it is worth noting the unprecedented modeling available to the senses of smell and taste.

This paper aims to answer the question of how artificial intelligence could help humans maintain and/or improve situational awareness in search and rescue operations at sea, as well as where in such processes artificial intelligence could be incorporated to most efficiently compensate for human vulnerabilities and support human strengths. In order to answer this, a joint cognitive system perspective has been adopted whilst joining in search and rescue practice operations. These operations have been observed and persons participating in them have been interviewed, in order to gather insights about the process and the persons conducting it. The results from these insights coupled with experience with artificial intelligence and automation, show that artificial intelligence could help improve and/or maintain situational awareness by adopting functions which take up unnecessary time from man. According to the joint cognitive system view, these functions should never be solely performed by artificial intelligence however, but in coagency with man; allocated functions should overlap between man and machine. Suggestions have been given regarding which functions in particular an artificial intelligent agent could perform in terms of search and rescue and where these functions occur in the process. None of these suggestions are without man involvement, as they should not be. To summarise, these suggestions include; a UAV equipped with an infrared camera to search large areas quickly, a decision support system equipped with image recognition to analyse images gathered from the UAV, as well as a communication tool which allows for shared search patterns and hotspots between search and rescue units. / WARA-PS

A robótica social representa um ramo da interação humano-robô que visa desenvolver robôs para atuar em ambientes não estruturados em parceria direta com seres humanos. O relatório A Roadmap for U.S. Robotics From Internet to Robotics, de 2013, preconiza a obtenção de resultados promissores em 12 anos desde que condições apropriadas sejam disponibilizadas para a área. Uma das condições envolve a utilização de ambiente de referência para desenvolver, avaliar e comparar o desempenho de sistemas cognitivos. Este ambiente é denominado Robot City com atores, cenários (casas, ruas, cidade) e auditores. Até o momento esse complexo ambiente não se concretizou, possivelmente devido ao elevado custo de implantação e manutenção de uma instalação desse porte. Nesta dissertação é proposto um caminho alternativo através da definição e implementação do simulador de sistemas cognitivos denominado Robot House Simulator (RHS). O simulador RHS tem como objetivo disponibilizar um ambiente residencial composto por sala e cozinha, no qual convivem dois agentes, um robô humanoide e um avatar humano. O agente humano é controlado pelo usuário do sistema e o robô é controlado por uma arquitetura cognitiva que determina o comportamento do robô. A arquitetura cognitiva estabelece sua percepção do ambiente através de informações sensoriais supridas pelo RHS e modeladas por uma ontologia denominada OntSense. A utilização de uma ontologia garante rigidez formal aos dados sensoriais além de viabilizar um alto nivel de abstração. O RHS tem como base a ferramenta de desenvolvimento de jogos Unity sendo aderente ao conceito de código aberto com disponibilização pelo repositório online GitHub. A validação do sistema foi realizada através de experimentos que demonstraram a capacidade do simulador em prover um ambiente de validação para arquiteturas cognitivas voltadas à robótica social. O RHS é pioneiro na integração de um simulador e uma arquitetura cognitiva, além disto, é um dos poucos direcionados para robótica social provendo rica informação sensorial, destacando-se o modelamento inédito disponibilizado para os sentidos de olfato e paladar. / Social robotics represents a branch of human-robot interaction that aims to develop robots to work in unstructured environments in direct partnership with humans. The Roadmap for Robotics from the Internet to Robotics, 2013, predicts achieving promising results in 12 years as long as appropriate conditions are made available to the area. One of the conditions involves the use of a reference environment to develop, evaluate and compare the performance of cognitive systems. This environment is called Robot City with actors, scenarios (houses, streets, city) and auditors. To date, this complex environment has not been materialized, possibly due to its high cost of installing and maintaining. In this dissertation an alternative way is proposed through the definition and implementation of the simulator of cognitive systems called Robot House Simulator (RHS). The RHS simulator aims to provide a residential environment composed of living room and kitchen, in which two agents live together, a humanoid robot and a human avatar. The human avatar is controlled by the user of the system and the robot is controlled by a cognitive architecture that determines the behavior of the robot. The cognitive architecture establishes its perception of the environment through sensorial information supplied by the RHS and modeled by an ontology called OntSense. The use of an ontology guarantees formal rigidity to the sensory data in addition to enabling a high level of abstraction. The RHS simulator is based on the Unity game engine and is adheres to the open source concept, available on the GitHub online repository. The validation of the system was performed through experiments that demonstrated the simulators ability to provide a validation environment for cognitive architectures aimed at social robotics. The RHS simulator is a pioneer in the integration of a simulator and a cognitive architecture. In addition, it is one of the few for social robotics to provide rich sensory information where it is worth noting the unprecedented modeling available to the senses of smell and taste.

Dans ces travaux, nous proposons un système de guidage automatique pour la navigation sûre d'un robot mobile dans un monde ouvert. Le principe est de contrôler la direction et la vitesse du véhicule afin de préserver son intégrité physique et celle de son environnement. Cela se traduit par la généralisation du concept d'obstacle permettant d'estimer l'espace de vitesses admissibles par le véhicule en fonction de la surface de navigation, des capacités du véhicule et de son état. Afin d'atteindre cet objectif, le système doit pour chaque itération : 1) fournir à la tâche de perception une zone sur laquelle elle devra focaliser son attention pour la reconstruction de l'environnement ; 2) générer des trajectoires admissibles par le véhicule ; 3) estimer le profil de vitesse admissible pour chacune d'entre elles ; 4) pour finir, sélectionner la plus optimale par rapport à un critère prédéfini. Des résultats simulés et obtenus sur un démonstrateur réel permettent d'analyser les performances obtenues du système face à des scénarios divers et en démontre la pertinence. / In this work, we propose an automatic guidance system for safe navigation of a mobile robot in an open environment. The principle is to control the direction and the speed oh the vehicle in order to preserve its physical integrity and that of its environment. That results in the generalization of obstacle's concept to estimate the admissible speeds of the vehicle taking into account the surface navigation, the capabilities of the vehicle and its state. To accomplish this objective, th system has to ; 1) provide to the perception task an area on witch it can focus its attention to build the environment, 2) generate acceptable trajectories by th vehicles ; 3) estimate the admissible speed profile for each of them, 4) finally, select the most optimal with respect to a predefined criterion. Simulated and real results show the performance of the system obtained against various scenarios.

<p>У дисертацији је представљен нови рачунарски модел<br />дуготрајне меморије, намењен за примене у конверзаци-<br />оним роботским агентима. Предложени модел је симболи-<br />чки, са методолошког аспекта, и инспирисан је изабраним<br />когнитивним механизмима људског меморијског система,<br />који укључују интеграцију менталних репрезентација,<br />семантичку категоризацију, асоцијативно учење и контек-<br />стно зависно селектовање информација. У основи модела<br />се налази симболички приступ за аутоматско моделовање<br />домена интеракције између човека и робота. Релевантни<br />функциoнaлни aспeкт предложеног модела oднoси се нa<br />прoблeме адекватног aктивирaњa делова дуготрајне<br />мeмoриje, у складу са спољашњим стимулансима, истори-<br />јом интеракције и тренутним контекстом интеракције. Ниво<br />апстракције у спецификацији модела је довољан да<br />омогући примену модела у широком спектру просторних,<br />униформних домена који су карактеристични за интеракцију<br />између човека и робота, а ниво детаља у спецификацији је<br />довољан за рачунарску имплементацију модела.</p> / <p>U disertaciji je predstavljen novi računarski model<br />dugotrajne memorije, namenjen za primene u konverzaci-<br />onim robotskim agentima. Predloženi model je simboli-<br />čki, sa metodološkog aspekta, i inspirisan je izabranim<br />kognitivnim mehanizmima ljudskog memorijskog sistema,<br />koji uključuju integraciju mentalnih reprezentacija,<br />semantičku kategorizaciju, asocijativno učenje i kontek-<br />stno zavisno selektovanje informacija. U osnovi modela<br />se nalazi simbolički pristup za automatsko modelovanje<br />domena interakcije između čoveka i robota. Relevantni<br />funkcionalni aspekt predloženog modela odnosi se na<br />probleme adekvatnog aktiviranja delova dugotrajne<br />memorije, u skladu sa spoljašnjim stimulansima, istori-<br />jom interakcije i trenutnim kontekstom interakcije. Nivo<br />apstrakcije u specifikaciji modela je dovoljan da<br />omogući primenu modela u širokom spektru prostornih,<br />uniformnih domena koji su karakteristični za interakciju<br />između čoveka i robota, a nivo detalja u specifikaciji je<br />dovoljan za računarsku implementaciju modela.</p> / <p>This dissertation proposes a novel computational model of<br />long-term memory intended for applications in conversational<br />robotic agents. The proposed model is symbolic, from the<br />methodological point of view, and cognitively-inspired by<br />selected cognitive mechanisms of the human memory system,<br />including integration of mental representations, semantic<br />categorization, associative learning, and context-dependent<br />information selection. In the core of the model there is a<br />symbolic approach to automatic modeling of domains of<br />human-robot interaction. The relevant functional aspect of the<br />proposed model concerns the problems of context-dependent<br />retrieval from long-term memory, in accordance with external<br />stimuli, the interaction history, and the current context of<br />interaction. The level of abstraction in the model is sufficient to<br />enable generalization of the model over a range of spatial,<br />uniform domains that are characterical for human-robot<br />interaction, while the level of detail contained in the<br />specification of the model is sufficient for a computational<br />implementation.</p>

No description available.

<p>A thesis study investigating the development and evaluation of a computer vision (CV) system for a manufacturing assembly task is reported. The CV inference results are compared to a Manufacturing Process Plan and an automation method completes a buyoff in the software, Solumina. Research questions were created and three hypotheses were tested. A literature review was conducted recognizing little consensus of Industry 4.0 technology adoption in manufacturing industries. Furthermore, the literature review uncovered the need for additional research within the topic of CV. Specifically, literature points towards more research regarding the cognitive capabilities of CV in manufacturing. A CV system was developed and evaluated to test for 90% or greater confidence in part detection. A CV dataset was developed and the system was trained and validated with it. Dataset contextualization was leveraged and evaluated, as per literature. A CV system was trained from custom datasets, containing six classes of part. The pre-contextualization dataset and post-contextualization dataset was compared by a Two-Sample T-Test and statistical significance was noted for three classes. A python script was developed to compare as-assembled locations with as-defined positions of components, per the Manufacturing Process Plan. A comparison of yields test for CV-based True Positives (TPs) and human-based TPs was conducted with the system operating at a 2σ level. An automation method utilizing Microsoft Power Automate was developed to complete the cognitive functionality of the CV system testing, by completing a buyoff in the software, Solumina, if CV-based TPs were equal to or greater than human-based TPs.</p>