RoboCup@Home: commanding a service robot by natural language.

It was in the ancient Greece that myths were written and, among already there one could nd the human desire of robotic servants. It was Hephaestus, god of technology, blacksmiths, craftsmen and artisans who is said to have built robots to help him on his workshop. This show how deep in our thoughts was this desire that one could nd stories and tales of human-shaped machines that danced in china or inanimate materials like mud that gave shape to golems in Jewish tradition. In the renaissance, a lot of automata began to arise, beginning by Leonardo Da Vinci to the artisans from China and Japan, mankind was trying to produce automatic machines, sometimes for their own bene t, some other times to their delight and fascination. But it wasn't until the digital era that the dream began to seem feasible. After millennia of wondering of automated robots, computers showed that automatic calculus was possible and from this, ideas of an automated mind arose. Theories for cognitive architectures are born since the early stages of arti cial intelligence, cognitive architectures that now are a reality. Thanks to the technological advances and the knowledge about the mind, what once was material for ctional tales, now is feasible and only matter of time. There is a lot of research on robotics and cognition that is beginning to get coupled into what are called "service robots". In this thesis, I present a system that participates in a competition designed for this kind of robots. A competition that have on its basis the same dream that humans have had all around the world for centuries: the cohabitation of humans and service automatons

Embodied Artificial Intelligence through Distributed Adaptive Control: An Integrated Framework

In this paper, we argue that the future of Artificial Intelligence research resides in two keywords: integration and embodiment. We support this claim by analyzing the recent advances of the field. Regarding integration, we note that the most impactful recent contributions have been made possible through the integration of recent Machine Learning methods (based in particular on Deep Learning and Recurrent Neural Networks) with more traditional ones (e.g. Monte-Carlo tree search, goal babbling exploration or addressable memory systems). Regarding embodiment, we note that the traditional benchmark tasks (e.g. visual classification or board games) are becoming obsolete as state-of-the-art learning algorithms approach or even surpass human performance in most of them, having recently encouraged the development of first-person 3D game platforms embedding realistic physics. Building upon this analysis, we first propose an embodied cognitive architecture integrating heterogenous sub-fields of Artificial Intelligence into a unified framework. We demonstrate the utility of our approach by showing how major contributions of the field can be expressed within the proposed framework. We then claim that benchmarking environments need to reproduce ecologically-valid conditions for bootstrapping the acquisition of increasingly complex cognitive skills through the concept of a cognitive arms race between embodied agents.Comment: Updated version of the paper accepted to the ICDL-Epirob 2017 conference (Lisbon, Portugal

The relationship between corporate governance mechanisms and firm value: evidence from the largest Australian firms

The mixed findings in the literature pertaining to the relationship between corporate governance mechanisms and firm value have resulted in the endogeneity issue of the former becoming central to discussions in corporate governance and corporate finance studies. As endogeneity can be in the form of reverse causality and/or in a dynamic sense, this thesis examines the relationships between corporate governance mechanisms that are proxied by ownership concentration and debt and firm value in the largest Australian firms from 1997 to 2008. The study investigates this issue through three different tests. First, the study examines whether there are any causal relationships between ownership concentration, debt and firm value. Second, the study investigates whether ownership concentration, debt and firm value are best treated as a group in order to assess their influence on each other. Therefore, the study assesses their substitutability or complementarity. Third, the study examines whether there are any non-linear relationships between ownership concentration and firm value on the one hand and debt on the other hand, as well as between debt and firm value. In investigating the dynamic endogeneity issue through these tests, the study employs two methodologies: two-way fixed effects (FE) and the two-step system generalised method of moments (GMM). In the first test, the study finds a causal relationship between ownership concentration and firm value as well as between debt and firm value. The causality is found to run from firm value to ownership concentration in a negative direction and from debt to firm value also in a negative direction. No causal relationship is found between ownership concentration and debt. However, further investigation by using sub-samples of ownership concentration reveals that there is causality between these two corporate governance mechanisms. It is found that causality runs from ownership concentration to debt in a negative direction. This test finds that firm value causes ownership concentration, thus providing evidence that endogeneity in the form of reverse causality exists. However, in the dynamic sense, it is found that dynamic endogeneity is not an issue in this test. The second test discovers that there is no evidence that ownership concentration, debt and firm value are effective as a group. Therefore, the study fails to identify their substitutability or complementarity. Furthermore, this test finds that dynamic endogeneity is not an issue in influencing ownership concentration, debt and firm value when they are tested as a group. In the final test, the study finds that there is a non-linear relationship between ownership concentration and firm value. This non-linear association is found to have an influence on the non-linearity between ownership concentration and debt. Further, the study also finds that debt and firm value are non-linear. It is found that the dynamic endogeneity issue does influence the non-linearity functions of ownership concentration but not the non-linearity functions of debt. The thesis concludes that dynamic endogeneity is not a serious issue in influencing the relationship between corporate governance mechanisms and firm value in the largest Australian firms

Learning mechanisms of uncertainty and neuromodulation

Learning systems are, by default, adaptive. Experience shapes the parame- ters of artificial systems, as well as it changes the connectivity of biological brains. Nonetheless, our attempts to create artificial learning systems have shown that continuous learning leads to overfitting recent data at the ex- pense of the older. While the field compensated this loss by segregating training from exploitation phases, this comes at the cost of sacrificing the adaptation to uncertain or new situations. How do animals robustly forage for food, find their lairs or flee from predators in ever-changing conditions and, sometimes unfamiliar situations? This dissertation proposes that our brains flexibly change between learning modes, favoring exploitation of previous knowledge or the incorporation/adaptation of new one. From the perspective of fine-tuning perception, this thesis presents a framework to unveil some of the mechanisms that biology can use to learn from uncertain situations rapidly. First, we identify two components of rapid learning by exploring how learning speed can be modulated not just explicitly (i.e., changing a learning rate parameter) but also implicitly (i.e., changing network dynamics) by the modulation of recurrent inhibitory networks. Studying the interactions of cholinergic neuromodulation with local and global inhibition allows us to differentiate between two operation modes that switch between robust exploitation of existing representations and flexibly exploring potential alternatives. To disambiguate the learning mechanisms behind this learning mode switching by a neuromodulator like acetylcholine, we take a step back and propose a neural model to estimate the input uncertainty. The resulting dynamical system minimizes the squared error relative to the input variance, as a proxy of how much an input was unexpected. We show how this kind of system uses two forms of inhibitory populations to estimate the input, and modulate the learning speed, in synthetic datasets and machine learning benchmarks. Altogether, this model illustrates a neural microcircuit, capable of flexibly incorporating new evidence when inputs are unexpected, facilitating learn- ing speed and providing a mechanism to externally regulating learning speed implicitly.Els sistemes d’aprenentatge son, per defecte, adaptatius. L’experiència dona forma als paràmetres dels sistemes artificials de la mateixa manera que canvia la connectivitat dels cervells biològics. Tot i aixı́, els intents per crear sistemes artificials d’aprenentatge ens ha ensenyat que l’aprenentatge continuat porta a el sobre-ajust de les dades més recents, al cost del més antic. Com poden els animals buscar menjar de forma robusta, trobar els seus caus o fugir dels depredadors? Aquesta tesi proposa que els cervells canvien de forma flexible entre modes d’aprenentatge, afavorint l’explotació del coneixement ja adquirit o la incorporació o adaptació amb nou coneixement. Des de la perspectiva del refinament de la percepció, aquesta tesi pre- senta un marc per desvelar alguns dels mecanismes que utilitza la biologia per a aprendre de situacions amb incertesa, de forma ràpida. Primer, iden- tifiquem dues components que permeten aprendre més ràpid, explorant com la velocitat d’aprenentatge pot ser modulada no només de forma explı́cita (i.e., modulant un paràmetre de velocitat d’aprenentatge) sinó també implı́cita (i.e., canviant les dinàmiques de la xarxa) a través de la modulació de les xarxes inhibitories amb recurrència. Mitjançant l’estu- di de les interaccions entre la neuromodulació colinèrgica i la inhibició local i global del cervell podem diferenciar entre dos modes d’operació que canvien entre l’explotació robusta de les representacions existents i l’exploració flexible de les potencials alternatives. Per a desambiguar els mecanismes d’aprenentatge que fan això possible, fem un pas enrere i proposem un model neuronal per estimar l’incertesa de la informació d’entrada a la xarxa. Aixı́ mostrem com un sistema com aquest requereix de l’us de dos poblacions inhibitòries diferents que prediuen les dades d’entrada i modulen la velocitat d’aprenentatge, tant en tasques sintètiques com benchmarks del camp d’aprenentatge automàtic (Machine Learning). En resum, aquest model esbossa un microcircuit neuronal capaç d’incor- porar nova evidència de forma flexible, quan les dades son inesperades, facilitant la velocitat d’aprenentatge i oferint un mecanisme per regular de forma externa però implicita, aquesta velocitat

RoboCup@Home: commanding a service robot by natural language.

It was in the ancient Greece that myths were written and, among already there one could nd the human desire of robotic servants. It was Hephaestus, god of technology, blacksmiths, craftsmen and artisans who is said to have built robots to help him on his workshop. This show how deep in our thoughts was this desire that one could nd stories and tales of human-shaped machines that danced in china or inanimate materials like mud that gave shape to golems in Jewish tradition. In the renaissance, a lot of automata began to arise, beginning by Leonardo Da Vinci to the artisans from China and Japan, mankind was trying to produce automatic machines, sometimes for their own bene t, some other times to their delight and fascination. But it wasn't until the digital era that the dream began to seem feasible. After millennia of wondering of automated robots, computers showed that automatic calculus was possible and from this, ideas of an automated mind arose. Theories for cognitive architectures are born since the early stages of arti cial intelligence, cognitive architectures that now are a reality. Thanks to the technological advances and the knowledge about the mind, what once was material for ctional tales, now is feasible and only matter of time. There is a lot of research on robotics and cognition that is beginning to get coupled into what are called "service robots". In this thesis, I present a system that participates in a competition designed for this kind of robots. A competition that have on its basis the same dream that humans have had all around the world for centuries: the cohabitation of humans and service automatons

Dinamic sensor system for climate measures

The following work presents the study and development of a meteorological measurement station adapted to be lifted on board an unmanned air vehicle (UAV). A direct sensor to microcontroller interface is used on the main measurement board to probe all the meteorological parameters through an assembler code. Data is then sent via radio signal to the ground base board which in turn decodes and processes data with Labview software to present it to the user in a understandable way. As the system is meant to be adaptable to the user necessities, a complete study of possible design alternatives and future improvements is also provided

Dinamic sensor system for climate measures

The following work presents the study and development of a meteorological measurement station adapted to be lifted on board an unmanned air vehicle (UAV). A direct sensor to microcontroller interface is used on the main measurement board to probe all the meteorological parameters through an assembler code. Data is then sent via radio signal to the ground base board which in turn decodes and processes data with Labview software to present it to the user in a understandable way. As the system is meant to be adaptable to the user necessities, a complete study of possible design alternatives and future improvements is also provided

Modeling Theory of Mind in Dyadic Games Using Adaptive Feedback Control

A major challenge in cognitive science and AI has been to understand how intelligent autonomous agents might acquire and predict the behavioral and mental states of other agents in the course of complex social interactions. How does such an agent model the goals, beliefs, and actions of other agents it interacts with? What are the computational principles to model a Theory of Mind (ToM)? Deep learning approaches to address these questions fall short of a better understanding of the problem. In part, this is due to the black-box nature of deep networks, wherein computational mechanisms of ToM are not readily revealed. Here, we consider alternative hypotheses seeking to model how the brain might realize a ToM. In particular, we propose embodied and situated agent models based on distributed adaptive control theory to predict the actions of other agents in five different game-theoretic tasks (Harmony Game, Hawk-Dove, Stag Hunt, Prisoner’s Dilemma, and Battle of the Exes). Our multi-layer control models implement top-down predictions from adaptive to reactive layers of control and bottom-up error feedback from reactive to adaptive layers. We test cooperative and competitive strategies among seven different agent models (cooperative, greedy, tit-for-tat, reinforcement-based, rational, predictive, and internal agents). We show that, compared to pure reinforcement-based strategies, probabilistic learning agents modeled on rational, predictive, and internal phenotypes perform better in game-theoretic metrics across tasks. The outlined autonomous multi-agent models might capture systems-level processes underlying a ToM and suggest architectural principles of ToM from a control-theoretic perspective

From motor to visually guided bimanual affordance learning

International audienceThe mechanisms of how the brain orchestrates multi-limb joint action have yet to be elucidated and few computational sensorimotor (SM) learning approaches have dealt with the problem of acquiring bimanual affordances. We propose a series of bidirectional (forward/inverse) SM maps and its associated learning processes that generalize from uni- to bimanual interaction (and affordances) naturally, reinforcing the motor equivalence property. The SM maps range from a SM nature to a solely sensory one: full body control, delta SM control (through small action changes), delta sensory co-variation (how body-related perceptual cues covariate with object-related ones). We make several contributions on how these SM maps are learned: (1) Context and Behavior-Based Babbling: generalizing goal babbling to the interleaving of absolute and local goals including guidance of reflexive behaviors; (2) Event-Based Learning: learning steps are driven by visual, haptic events; and (3) Affordance Gradients: the vectorial field gradients in which an object can be manipulated. Our modeling of bimanual affordances is in line with current robotic research in forward visuomotor mappings and visual servoing, enforces the motor equivalence property, and is also consistent with neurophysiological findings like the multiplicative encoding scheme

iCub-HRI: A coherent framework for complex human-robot interaction scenarios on the iCub humanoid robot

Generating complex, human-like behaviour in a humanoid robot like the iCub requires the integration of a wide range of open source components and a scalable cognitive architecture. Hence, we present the iCub-HRI library which provides convenience wrappers for components related to perception (object recognition, agent tracking, speech recognition, touch detection), object manipulation (basic and complex motor actions) and social interaction (speech synthesis, joint attention) exposed as C++ library with bindings for Python and Java (Matlab). In addition to previously integrated components, the library allows for simple extension to new components and rapid prototyping by adapting to changes in interfaces between components. We also provide a set of modules which make use of the library, such as a high-level knowledge acquisition module and an action recognition module. The proposed architecture has been successfully employed for a complex human-robot interaction scenario involving the acquisition of language capabilities, execution of goal-oriented behaviour and expression of a verbal narrative of the robot's experience in the world. Accompanying this paper is a tutorial which allows a subset of this interaction to be reproduced. The architecture is aimed at researchers familiarising themselves with the iCub ecosystem, as well as expert users, and we expect the library to be widely used in the iCub community