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A Database for Learning Numbers by Visual Finger Recognition in developmental Neuro-robotics

FRONTIERS IN NEUROrobotics 2021年 15卷 619504页

作者： Davies, Sergio Lucas, Alexandr Ricolfe-Viala, Carlos Di Nuovo, Alessandro Sheffield Hallam Univ Dept Comp Sheffield S Yorkshire England Univ Sheffield Dept Comp Sci Sheffield S Yorkshire England Univ Politecn Valencia Inst Automat & Informat Ind Valencia Spain

Numerical cognition is a fundamental component of human intelligence that has not been fully understood yet. Indeed, it is a subject of research in many disciplines, e.g., neuroscience, education, cognitive and developmental psychology, philosophy of mathematics, linguistics. In Artificial Intelligence, aspects of numerical cognition have been modelled through neural networks to replicate and analytically study children behaviours. However, artificial models need to incorporate realistic sensory-motor information from the body to fully mimic the children's learning behaviours, e.g., the use of fingers to learn and manipulate numbers. To this end, this article presents a database of images, focused on number representation with fingers using both human and robot hands, which can constitute the base for building new realistic models of numerical cognition in humanoid robots, enabling a grounded learning approach in developmental autonomous agents. The article provides a benchmark analysis of the datasets in the database that are used to train, validate, and test five state-of-the art deep neural networks, which are compared for classification accuracy together with an analysis of the computational requirements of each network. The discussion highlights the trade-off between speed and precision in the detection, which is required for realistic applications in robotics.

关键词： cognitive robotics region-based CNN SSD single shot detector finger counting iCub robot developmental robotics developmental neuro-robotics

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Symbolic Manipulation Planning With Discovered Object and Relational Predicates

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IEEE robotics AND AUTOMATION LETTERS 2025年第2期10卷 1968-1975页

作者： Ahmetoglu, Alper Oztop, Erhan Ugur, Emre Brown Univ Dept Comp Sci Providence RI 02912 USA Ozyegin Univ Dept Comp Sci TR-34794 Istanbul Turkiye Osaka Univ OTRI SISReC Osaka 5650871 Japan Bogazici Univ Dept Comp Engn TR-34342 Istanbul Turkiye

Discovering the symbols and rules that can be used in long-horizon planning from a robot's unsupervised exploration of its environment and continuous sensorimotor experience is a challenging task. The previous studies proposed learning symbols from single or paired object interactions and planning with these symbols. In this work, we propose a system that learns rules with discovered object and relational symbols that encode an arbitrary number of objects and the relations between them, converts those rules to Planning Domain Description Language (PDDL), and generates plans that involve affordances of the arbitrary number of objects to achieve tasks. We validated our system with box-shaped objects in different sizes and showed that the system can develop a symbolic knowledge of pick-up, carry, and place operations, taking into account object compounds in different configurations, such as boxes would be carried together with a larger box that they are placed on. We also compared our method with other symbol learning methods and showed that planning with the operators defined over relational symbols gives better planning performance compared to the baselines.

关键词： developmental robotics learning categories and concepts deep learning methods developmental robotics learning categories and concepts deep learning methods

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developmental analysis in evolutionary robotics

Developmental analysis in evolutionary robotics

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IEEE Mountain Workshop on Adaptive and Learning Systems

作者： Nelson, A. L. Grant, E. Androt LLC POB 44065 Tucson AZ 85733 USA North Carolina State Univ Dept ECE Raleigh NC 27695 USA

ISBN: (纸本)1424401666

This paper presents a developmental analysis of robot controllers created using evolutionary robotics (ER) methods. ER uses artificial evolution to automatically design and synthesize intelligent robot controllers. An aggregate fitness function that injects relatively little a priori task knowledge into the evolving controllers was used. We analyze the course of development of robot controllers evolving to perform a competitive goal-locating task. To sample the course of evolution, controllers were taken from progressively more advanced generations, and were then tested in a novel environment. Developments and changes in the controllers' abilities and competencies were identified and correlated with overall controller fitness. As evolution progressed, it was found that robots evolved more complex high-level behaviors that were not explicitly selected for by the fitness function.

关键词： evolutionary robotics developmental robotics evolutionary neural networks swarm robotics

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Comparison Studies on Active Cross-Situational Object-Word Learning Using Non-Negative Matrix Factorization and Latent Dirichlet Allocation

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IEEE TRANSACTIONS ON COGNITIVE AND developmental SYSTEMS 2018年第4期10卷 1023-1034页

作者： Chen, Yuxin Bordes, Jean-Baptiste Filliat, David ENSTA ParisTech Unite Informat & Ingn Syst F-91120 Palaiseau France INRIA Flowing Epigenet Robots & Syst Team F-78153 Rocquencourt France Univ Paris Saclay F-91120 Palaiseau France Univ Paris Saclay Ecole Polytech F-91120 Palaiseau France

Future intelligent robots are expected to be able to adapt continuously to their environment. For this purpose, recognizing new objects and learning new words through interactive learning with humans is fundamental. Such setup results in ambiguous teaching data which humans have been shown to address using cross-situational learning, i.e., by analyzing common factors between multiple learning situations. Moreover, they have been shown to be more efficient when actively choosing the learning samples, e.g., which object they want to learn. Implementing such abilities on robots can be performed by latent-topic learning models such as non-negative matrix factorization or latent Dirichlet allocation. These cross-situational learning methods tackle referential and linguistic ambiguities, and can be associated with active learning strategies. We propose two such methods: 1) the maximum reconstruction error-based selection and 2) confidence base exploration. We present extensive experiments using these two learning algorithms through a systematic analysis on the effects of these active learning strategies in contrast with random choice. In addition, we study the factors underlying the active learning by focusing on the use of sample repetition, one of the learning behaviors that have been shown to be important for humans.

关键词： Active learning cross-situational learning developmental robotics latent Dirichlet association (LDA) non-negative matrix factorization (NMF) word-referent learning

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HMM-based concept learning for a mobile robot

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2007年第2期11卷 199-212页

作者： Squire, Kevin M. Levinson, Stephen E. Naval Postgrad Sch Monterey CA 93942 USA Univ Urbana Urbana IL 61801 USA

We are developing an intelligent robot and attempting to teach it language. While there are many aspects of this research, for the purposes here the most important are the following ideas. Language is primarily based on semantics, not syntax, which is still the focus in speech recognition research these days. To truly learn meaning, a language engine cannot simply be a computer program running on a desktop computer analyzing speech. It must be part of a more general, embodied intelligent system, one capable of using associative learning to form concepts from the perception of experiences in the world, and further capable of manipulating those concepts symbolically. In this paper, we present a general cascade model for learning concepts, and explore the use of hidden Markov models (HMMs) as part of the cascade model. HMMs are capable of automatically learning and extracting the underlying structure of continuous-valued inputs and representing that structure in the states of the model. These states can then be treated as symbolic representations of the inputs. We show how a cascade of HMMs can be embedded in a small mobile robot and used to find correlations among sensory inputs to learn a set of symbolic concepts, which are used for decision making and could eventually be manipulated linguistically.

关键词： developmental robotics hidden Markov models (HMMs) hierarchical model online learning semantic learning

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Object Learning Through Active Exploration

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IEEE TRANSACTIONS ON AUTONOMOUS MENTAL DEVELOPMENT 2014年第1期6卷 56-72页

作者： Ivaldi, Serena Sao Mai Nguyen Lyubova, Natalia Droniou, Alain Padois, Vincent Filliat, David Oudeyer, Pierre-Yves Sigaud, Olivier CNRS UMR 7222 ISIR Paris France Univ Paris 06 Paris France INRIA Flowers Team Bordeaux Sud Ouest France ENSTA ParisTech Flowers Team Paris France

This paper addresses the problem of active object learning by a humanoid child-like robot, using a developmental approach. We propose a cognitive architecture where the visual representation of the objects is built incrementally through active exploration. We present the design guidelines of the cognitive architecture, its main functionalities, and we outline the cognitive process of the robot by showing how it learns to recognize objects in a human-robot interaction scenario inspired by social parenting. The robot actively explores the objects through manipulation, driven by a combination of social guidance and intrinsic motivation. Besides the robotics and engineering achievements, our experiments replicate some observations about the coupling of vision and manipulation in infants, particularly how they focus on the most informative objects. We discuss the further benefits of our architecture, particularly how it can be improved and used to ground concepts.

关键词： Active exploration developmental robotics human-robot interaction

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developmental learning for autonomous robots

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robotics AND AUTONOMOUS SYSTEMS 2007年第9期55卷 750-759页

作者： Lee, M. H. Meng, Q. Chao, F. Univ Wales Dept Comp Sci Aberystwyth SY23 3BD Dyfed Wales Loughborough Univ Technol Dept Comp Sci Loughborough LE11 3TU Leics England

developmental robotics is concerned with the design of algorithms that promote robot adaptation and learning through qualitative growth of behaviour and increasing levels of competence. This paper uses ideas and inspiration from early infant psychology (up to three months of age) to examine how robot systems could discover the structure of their local sensory-motor spaces and learn how to coordinate these for the control of action. An experimental learning model is described and results from robotic experiments using the model are presented and discussed. (c) 2007 Elsevier B.V. All rights reserved.

关键词： sensory-motor coordination developmental robotics early infant learning

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Staged Development of Robot Skills: Behavior Formation, Affordance Learning and Imitation with Motionese

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IEEE TRANSACTIONS ON AUTONOMOUS MENTAL DEVELOPMENT 2015年第2期7卷 119-139页

作者： Ugur, Emre Nagai, Yukie Sahin, Erol Oztop, Erhan Univ Innsbruck Inst Comp Sci A-6020 Innsbruck Austria Osaka Univ Grad Sch Engn Emergent Robot Lab Osaka Japan Middle E Tech Univ KOVAN Res Lab Dept Comp Engn TR-06531 Ankara Turkey Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA Ozyegin Univ Dept Comp Sci Istanbul Turkey ATR Dynam Brain Imaging Dept Tokyo Japan

Inspired by infant development, we propose a three staged developmental framework for an anthropomorphic robot manipulator. In the first stage, the robot is initialized with a basic reach-and-enclose-on-contact movement capability, and discovers a set of behavior primitives by exploring its movement parameter space. In the next stage, the robot exercises the discovered behaviors on different objects, and learns the caused effects;effectively building a library of affordances and associated predictors. Finally, in the third stage, the learned structures and predictors are used to bootstrap complex imitation and action learning with the help of a cooperative tutor. The main contribution of this paper is the realization of an integrated developmental system where the structures emerging from the sensorimotor experience of an interacting real robot are used as the sole building blocks of the subsequent stages that generate increasingly more complex cognitive capabilities. The proposed framework includes a number of common features with infant sensorimotor development. Furthermore, the findings obtained from the self-exploration and motionese guided human-robot interaction experiments allow us to reason about the underlying mechanisms of simple-to-complex sensorimotor skill progression in human infants.

关键词： Affordance developmental robotics goal emulation imitation motionese sensorimotor learning

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Self-detection in robots: a method based on detecting temporal contingencies

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ROBOTICA 2011年第1期29卷 1-21页

作者： Stoytchev, Alexander Iowa State Univ Dev Robot Lab Dept Elect & Comp Engn Ames IA 50011 USA

This paper addresses the problem of self-detection by a robot. The paper describes a methodology for autonomous learning of the characteristic delay between motor commands (efferent signals) and observed movements of visual stimuli (afferent signals). The robot estimates its own efferent-afferent delay from self-observation data gathered while performing motor babbling, i.e., random rhythmic movements similar to the primary circular reactions described by Piaget. After the efferent-afferent delay is estimated, the robot imprints on that delay and can later use it to successfully classify visual stimuli as either "self" or "other." Results from robot experiments performed in environments with increasing degrees of difficulty are reported.

关键词： Self-detection Self/other discrimination developmental robotics Behavior-based robotics Autonomous robotics

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From unknown sensors and actuators to actions grounded in sensorimotor perceptions

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CONNECTION SCIENCE 2006年第2期18卷 121-144页

作者： Olsson, Lars A. Nehaniv, Chrystopher L. Polani, Daniel Univ Hertfordshire Sch Comp Sci Adapt Syst Res Grp Hatfield AL10 9AB Herts England Univ Hertfordshire Sch Comp Sci Algorithms Res Grp Hatfield AL10 9AB Herts England

This article describes a developmental system based on information theory implemented on a real robot that learns a model of its own sensory and actuator apparatus. There is no innate knowledge regarding the modalities or representation of the sensory input and the actuators, and the system relies on generic properties of the robot's world, such as piecewise smooth effects of movement on sensory changes. The robot develops the model of its sensorimotor system by first performing random movements to create an informational map of the sensors. Using this map, the robot then learns what effects the different possible actions have on the sensors. After this developmental process, the robot can perform basic visually guided movement.

关键词： developmental robotics information theory emergence of structure

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