Evaluation of Word Representations in Grounding Natural Language Instructions through Computational Human-Robot Interaction

International audienceIn order to interact with people in a natural way, a robot must be able to link words to objects and actions. Although previous studies in the literature have investigated grounding, they did not consider grounding of unknown synonyms. In this paper, we introduce a probabilistic model for grounding unknown synonymous object and action names using cross-situational learning. The proposed Bayesian learning model uses four different word representations to determine synonymous words. Afterwards, they are grounded through geometric characteristics of objects and kinematic features of the robot joints during action execution. The proposed model is evaluated through an interaction experiment between a human tutor and HSR robot. The results show that semantic and syntactic information enable grounding of unknown synonyms and that the combination of both achieves the best grounding

The Ecology of Open-Ended Skill Acquisition: Computational framework and experiments on the interactions between environmental, adaptive, multi-agent and cultural dynamics

An intriguing feature of the human species is our ability to continuously invent new problems and to proactively acquiring new skills in order to solve them: what is called open-ended skill acquisition (OESA). Understanding the mechanisms underlying OESA is an important scientific challenge in both cognitive science (e.g. by studying infant cognitive development) and in artificial intelligence (aiming at computational architectures capable of open-ended learning). Both fields, however, mostly focus on cognitive and social mechanisms at the scale of an individual’s life. It is rarely acknowledged that OESA, an ability that is fundamentally related to the characteristics of human intelligence, has been necessarily shaped by ecological, evolutionary and cultural mechanisms interacting at multiple spatiotemporal scales. In this thesis, I present a research program aiming at understanding, modelingand simulating the dynamics of OESA in artificial systems, grounded in theories studying its eco-evolutionary bases in the human species. It relies on a conceptual framework expressing the complex interactions between environmental, adaptive, multi-agent and cultural dynamics. Three main research questions are developed and I present a selection of my contributions for each of them.- What are the ecological conditions favoring the evolution of skill acquisition?- How to bootstrap the formation of a cultural repertoire in populations of adaptive agents?- What is the role of cultural evolution in the open-ended dynamics of human skill acquisition?By developing these topics, we will reveal interesting relationships between theories in human evolution and recent approaches in artificial intelligence. This will lead to the proposition of a humanist perspective on AI: using it as a family of computational tools that can help us to explore and study the mechanisms driving open-ended skill acquisition in both artificial and biological systems, as a way to better understand the dynamics of our own species within its whole ecological context. This document presents an overview of my scientific trajectory since the start of my PhD thesis in 2007, the detail of my current research program, a selection of my contributions as well as perspectives for future work

Self-Organization of Early Vocal Development in Infants and Machines: The Role of Intrinsic Motivation

International audienceWe bridge the gap between two issues in infant development: vocal development and intrinsic motivation. We propose and experimentally test the hypothesis that general mechanisms of intrinsically motivated spontaneous exploration, also called curiosity-driven learning, can self-organize developmental stages during early vocal learning. We introduce a computational model of intrinsically motivated vocal exploration, which allows the learner to autonomously structure its own vocal experiments, and thus its own learning schedule, through a drive to maximize competence progress. This model relies on a physical model of the vocal tract, the auditory system and the agent's motor control as well as vocalizations of social peers. We present computational experiments that show how such a mechanism can explain the adaptive transition from vocal self-exploration with little influence from the speech environment, to a later stage where vocal exploration becomes influenced by vocalizations of peers. Within the initial self-exploration phase, we show that a sequence of vocal production stages self-organizes, and shares properties with data from infant developmental psychology: the vocal learner first discovers how to control phonation, then focuses on vocal variations of unarticulated sounds, and finally automatically discovers and focuses on babbling with articulated proto-syllables. As the vocal learner becomes more proficient at producing complex sounds, imitating vocalizations of peers starts to provide high learning progress explaining an automatic shift from self-exploration to vocal imitation

Control strategies for cleaning robots in domestic applications: A comprehensive review:

Service robots are built and developed for various applications to support humans as companion, caretaker, or domestic support. As the number of elderly people grows, service robots will be in increasing demand. Particularly, one of the main tasks performed by elderly people, and others, is the complex task of cleaning. Therefore, cleaning tasks, such as sweeping floors, washing dishes, and wiping windows, have been developed for the domestic environment using service robots or robot manipulators with several control approaches. This article is primarily focused on control methodology used for cleaning tasks. Specifically, this work mainly discusses classical control and learning-based controlled methods. The classical control approaches, which consist of position control, force control, and impedance control , are commonly used for cleaning purposes in a highly controlled environment. However, classical control methods cannot be generalized for cluttered environment so that learning-based control methods could be an alternative solution. Learning-based control methods for cleaning tasks can encompass three approaches: learning from demonstration (LfD), supervised learning (SL), and reinforcement learning (RL). These control approaches have their own capabilities to generalize the cleaning tasks in the new environment. For example, LfD, which many research groups have used for cleaning tasks, can generate complex cleaning trajectories based on human demonstration. Also, SL can support the prediction of dirt areas and cleaning motion using large number of data set. Finally, RL can learn cleaning actions and interact with the new environment by the robot itself. In this context, this article aims to provide a general overview of robotic cleaning tasks based on different types of control methods using manipulator. It also suggest a description of the future directions of cleaning tasks based on the evaluation of the control approaches

Sensorimotor input as a language generalisation tool: a neurorobotics model for generation and generalisation of noun-verb combinations with sensorimotor inputs

The paper presents a neurorobotics cognitive model explaining the understanding and generalisation of nouns and verbs combinations when a vocal command consisting of a verb-noun sentence is provided to a humanoid robot. The dataset used for training was obtained from object manipulation tasks with a humanoid robot platform; it includes 9 motor actions and 9 objects placing placed in 6 different locations), which enables the robot to learn to handle real-world objects and actions. Based on the multiple time-scale recurrent neural networks, this study demonstrates its generalisation capability using a large data-set, with which the robot was able to generalise semantic representation of novel combinations of noun-verb sentences, and therefore produce the corresponding motor behaviours. This generalisation process is done via the grounding process: different objects are being interacted, and associated, with different motor behaviours, following a learning approach inspired by developmental language acquisition in infants. Further analyses of the learned network dynamics and representations also demonstrate how the generalisation is possible via the exploitation of this functional hierarchical recurrent network

Natural task learning through simultaneous language grounding and action learning

Artificial agents and in particular robots, i.e. agents with some form of embodiment, provide nearly unlimited possibilities to support humans in their daily lives by reliably performing hazardous, repetitive, and physically demanding tasks, removing the risk of human errors, and providing social, mental, and physical care as needed, and around-the-clock. However, for this, artificial agents need to be able to communicate with other agents, in particular humans, in a natural and efficient manner, and to autonomously learn new tasks. The most natural way for humans to tell another agent to perform a task or to explain how to perform a task is through natural language. Therefore, artificial agents need to be able to understand natural language, i.e. extract the meanings of words and phrases, which requires words and phrases to be linked to their corresponding percepts through grounding. Theoretically, groundings, i.e. connections between words and percepts, can be manually specified, however, in practice this is not possible due to the complexity and dynamicity of human-centered environments, like private homes or supermarkets, and the ambiguity inherent to natural language, e.g. synonymy and homonymy. Therefore, agents need to be able to autonomously obtain new groundings and continuously update existing groundings to account for changes in the environment and incorporate new information obtained through the agent’s sensors. Furthermore, the obtained groundings should be utilizable to learn new tasks from natural language instructions. Therefore, this thesis proposes a novel framework for simultaneous language grounding and action learning that achieves three main objectives. First, it enables agents to continuously ground synonymous words and phrases without requiring external support by another agent. Second, it enables agents to utilize external support, if available, without depending on it. Finally, it enables agents to utilize previously learned groundings to learn new tasks from language instructions

A Curious Robot Learner for Interactive Goal-Babbling (Strategically Choosing What, How, When and from Whom to Learn)

Les dé s pour voir des robots opérant dans l environnement de tous les jours des humains et sur unelongue durée soulignent l importance de leur adaptation aux changements qui peuvent être imprévisiblesau moment de leur construction. Ils doivent être capable de savoir quelles parties échantillonner, et quelstypes de compétences il a intérêt à acquérir. Une manière de collecter des données est de décider par soi-même où explorer. Une autre manière est de se référer à un mentor. Nous appelons ces deux manièresde collecter des données des modes d échantillonnage. Le premier mode d échantillonnage correspondà des algorithmes développés dans la littérature pour automatiquement pousser l agent vers des partiesintéressantes de l environnement ou vers des types de compétences utiles. De tels algorithmes sont appelésdes algorithmes de curiosité arti cielle ou motivation intrinsèque. Le deuxième mode correspond au guidagesocial ou l imitation, où un partenaire humain indique où explorer et où ne pas explorer.Nous avons construit une architecture algorithmique intrinsèquement motivée pour apprendre commentproduire par ses actions des e ets et conséquences variées. Il apprend de manière active et en ligne encollectant des données qu il choisit en utilisant plusieurs modes d échantillonnage. Au niveau du metaapprentissage, il apprend de manière active quelle stratégie d échantillonnage est plus e cace pour améliorersa compétence et généraliser à partir de son expérience à un grand éventail d e ets. Par apprentissage parinteraction, il acquiert de multiples compétences de manière structurée, en découvrant par lui-même lesséquences développementale.The challenges posed by robots operating in human environments on a daily basis and in the long-termpoint out the importance of adaptivity to changes which can be unforeseen at design time. The robot mustlearn continuously in an open-ended, non-stationary and high dimensional space. It must be able to knowwhich parts to sample and what kind of skills are interesting to learn. One way is to decide what to exploreby oneself. Another way is to refer to a mentor. We name these two ways of collecting data sampling modes.The rst sampling mode correspond to algorithms developed in the literature in order to autonomously drivethe robot in interesting parts of the environment or useful kinds of skills. Such algorithms are called arti cialcuriosity or intrinsic motivation algorithms. The second sampling mode correspond to social guidance orimitation where the teacher indicates where to explore as well as where not to explore. Starting fromthe study of the relationships between these two concurrent methods, we ended up building an algorithmicarchitecture with a hierarchical learning structure, called Socially Guided Intrinsic Motivation (SGIM).We have built an intrinsically motivated active learner which learns how its actions can produce variedconsequences or outcomes. It actively learns online by sampling data which it chooses by using severalsampling modes. On the meta-level, it actively learns which data collection strategy is most e cient forimproving its competence and generalising from its experience to a wide variety of outcomes. The interactivelearner thus learns multiple tasks in a structured manner, discovering by itself developmental sequences.BORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF

Development of New Cotton Defoliation Sprayer Using Unmanned Ground Vehicle and Pulse Width Modulation Technology

Chemical spraying is one of the most important and frequently performed intercultural agriculture operations. It is imperative to utilize appropriate spraying technology as a selection of ineffective one leads to waste of agrochemicals to the non‐target area. Several precision technologies have been developed in the past few decades, such as image processing based on real‐time variable‐rate chemical spraying systems, autonomous chemical sprayers using machine vision and nozzle control, and use of unmanned aerial and ground vehicles. Cotton (Gossypium hirsutum L.) is an important industrial crop. It is a perennial crop with indeterminate growth habit; however, in most parts of the United States, it is grown as an annual crop and managed using growth regulators. Cotton defoliation is a natural physiological phenomenon, but untimely and/or inadequate defoliation by natural processes necessitates the application of chemical defoliants for efficient harvest. Defoliation is a major production practice influencing harvester efficiency, fiber trash content, cotton yield, and fiber quality. Currently, defoliant spraying is done by conventional ground driven boom sprayer or aerial applicator and both systems spray chemical vertically downwards into the canopy, which results in less chemical reaching the bottom of the canopy. Thus, a new autonomous ground sprayer was developed using robotics and pulse width modulation, which travels between two rows covering the whole canopy of the plant. Field research was conducted to evaluate the (i) effect of duty cycles (20%,40%, and 60%) on droplet characteristic (droplet distribution, deposition, and drift potential), defoliation cotton fiber and (ii) effect of duty cycles on cotton yield and II fiber quality. Droplet characteristics (droplet distribution, density, and potential droplet drift) were non-significant across the treatments and results from the water‐sensitive paper field test showed adequate penetration with low flow rates. Therefore, a 20% duty cycle was sufficient to defoliate based on the result of the field experiment. Likewise, the defoliants could be applied safely at the duty cycles tested without influencing fiber quality except for nep/gm, length (Ln), L (5%), short fiber content (SFCn), trash content in field 1 and micronaire, nep size, length (Ln), span length (5%), SFC, and fiber fineness in field 2 which were significant. However, the 20% duty cycle significantly reduced the amount of defoliant and would be a good choice for the autonomous cotton defoliation. This is a significant development as there is a huge potential to save on the cost of applying defoliant chemicals and the environment

Vers un modèle biologiquement plausible de la sélection de l'action pour un robot mobile

This thesis aims at studying the different mechanisms involved in action selection and de-cision making processes, according to animal experiments and neurobiological recordings. Forthat matter, we propose several biologically plausible models for action selection. The goal is toachieve a better understanding of the animal’s brain functions. This gives us the opportunity todevelop bioinspired control architectures for robots that are more robust and adaptative to a realenvironement. These models are based on Artificial Neural Networks, allowing us to test ourhypotheses on simulations of different brain regions and function, implemented on robots andvirtual agents.Action selection for mobile robots can be approached from different angles. This processcan be seen as the selection between two possibilities, e.g. go left or go right. Those mechanismsinvolve the ability to learn and categorize specific events, encoding contexts where a change inthe perception is perceived, a change in the behavior is noticed or the decision is made. There-fore, this thesis studies those capacities of acquisition, categorisation and coding of differentevents that can be relevant for action selection.We also, approach the action selection as a strategy selection. The different behaviors areguided consciously or through automated behavior learned as habits. We investigate differentpossibilities allowing a robot to develop those capacities. Also, we aim at studying interactionsthat can emerge between those mechanisms during navigational behaviors.The work presented in this these is based on the modelisation of the hippocampo-cotico-basal loops involved in the navigational behaviors, the action selection and the multimodal cat-egorisation of events. We base our models on a previous model of the hippocampus for thelearning of spatio-temporal associations and for multimodal conditionning of perceptive events.It is based on sensorimotor associations between place cells and actions to achieve navigationalbehaviors. The model involves also a specific type of hippocampic cells, named transition cells,for temporal prediction of future events. This capacity allows the model to learn spatio-temporalsequences, and it represents the neural substrate for the learning of a cognitive map, hypoth-esised to be localized in prefrontal and/or parietal areas. This kind of topological map allowsto plan the behavior of the robot according to its motivations, which is used in goal orientedexperiments to achieve goals and capture rewardsCette thèse étudie les mécanismes de sélection de l’action et de choix de stratégie tels qu’ilsapparaissent à travers des expériences animales et des enregistrements neurobiologiques. Nousproposons ensuite des modèles biologiquement plausibles de la sélection de l’action. L’objectifest de mieux comprendre le fonctionnement du cerveau chez les êtres vivants et de pouvoir endéduire des architectures de contrôle bio-inspirées, plus robustes et adaptées à l’environnement.Les modèles étudiés sont réalisés avec des réseaux de neurones artificiels, permettant de mod-éliser des régions cérébrales et ainsi pouvoir simuler le fonctionnement du cerveau, ce qui permetde tester nos hypothèses sur des robots et des agents virtuels.L’étude de la sélection de l’action pour des robots mobiles implique plusieurs approches. Lasélection de l’action peut être étudiée du point de vue du choix entre plusieurs actions basiques,e.g. un choix binaire aller à gauche ou à droite.Ceci passe forcément par l’acquisition et la catégorisation d’instants et d’événements spéciaux,perçus ou effectués, qui représentent des contextes dans lesquels la perception change, le com-portement est modifié ou bien la sélection est réalisée. Ainsi, la thèse traite aussi de l’acquisition,la catégorisation et l’encodage de ces événements importants dans la sélection de l’action.Enfin, on s’intéressera à la sélection de l’action du point de vue de la sélection de stratégie.Les différents comportements peuvent être dirigés consciemment ou bien être des automatismesacquis avec l’habitude. Le but ici est d’explorer différentes approches pour que le robot puissedévelopper ces deux capacités, mais aussi d’étudier les interactions entre ces types de mécan-ismes dans la cadre de tâches de navigation.Les travaux de cette thèse se basent sur la modélisation du fonctionnement de différentesboucles hippocampo-cortico-basales impliquées dans des tâches de navigation, de sélection del’action et de catégorisations multimodales. En particulier, nous avons un modèle de l’hippocampepermettant d’apprendre des associations spatio-temporelles et des conditionnements multimodauxentre des événements perceptifs. Il se base sur des associations sensorimotrices entre des cellulesappelées cellules de lieu qui sont associées avec des actions pour définir des comportements co-hérents. Le modèle fait aussi intervenir des cellules de transition hippocampiques, permettant defaire des prédictions temporelles sur les événements futurs. Celles-ci permettent l’apprentissagede séquences spatio-temporelles, notamment du fait qu’elles représentent le substrat neuronal àl’apprentissage d’une carte cognitive, située elle au niveau du cortex préfrontal et/ou pariétal.Ce type de carte permet de planifier des chemins à suivre en fonction des motivations du robot,ce qui permet de rejoindre différents buts précédemment découverts dans l’environnement