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A Procedural Constructive Learning Mechanism with Deep Reinforcement Learning for Cognitive Agents

JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2024年第1期110卷 38-38页

作者： Rossi, Leonardo de Lellis Rohmer, Eric Costa, Paula Dornhofer Paro Colombini, Esther Luna Simoes, Alexandre da Silva Gudwin, Ricardo Ribeiro Univ Estadual Campinas Fac Engn Elect & Comp FEEC Unicamp Campinas Brazil Univ Estadual Campinas Inst Comp IC Unicamp Campinas Brazil Univ Estadual Paulista Unesp Dept Engn Controle & Automacao DECA Inst Ciencia & Tecnol Sorocaba ICTS Campus Sorocaba Sorocaba SP Brazil Univ Estadual Campinas Hub Artificial Intelligence & Cognit Architectures Campinas Brazil

Recent advancements in AI and deep learning have created a growing demand for artificial agents capable of performing tasks within increasingly complex environments. To address the challenges associated with continuous learning constraints and knowledge capacity in this context, cognitive architectures inspired by human cognition have gained significance. This study contributes to existing research by introducing a cognitive-attentional system employing a constructive neural network-based learning approach for continuous acquisition of procedural knowledge. We replace an incremental tabular Reinforcement Learning algorithm with a constructive neural network deep reinforcement learning mechanism for continuous sensorimotor knowledge acquisition, thereby enhancing the overall learning capacity. The primary emphasis of this modification centers on optimizing memory utilization and reducing training time. Our study presents a learning strategy that amalgamates deep reinforcement learning with procedural learning, mirroring the incremental learning process observed in human sensorimotor development. This approach is embedded within the CONAIM cognitive-attentional architecture, leveraging the cognitive tools of CST. The proposed learning mechanism allows the model to dynamically create and modify elements in its procedural memory, facilitating the reuse of previously acquired functions and procedures. Additionally, it equips the model with the capability to combine learned elements to effectively adapt to complex scenarios. A constructive neural network was employed, initiating with an initial hidden layer comprising one neuron. However, it possesses the capacity to adapt its internal architecture in response to its performance in procedural and sensorimotor learning tasks, inserting new hidden layers or neurons. Experimentation conducted through simulations involving a humanoid robot demonstrates the successful resolution of tasks that were previously unsolved through i

关键词： Cognitive architecture Neural networks Deep reinforcement learning developmental robotics

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Systematic Review of Affective Computing Techniques for Infant Robot Interaction

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INTERNATIONAL JOURNAL OF SOCIAL robotics 2023年第3期15卷 393-409页

作者： Filippini, Chiara Merla, Arcangelo Univ G dAnnunzio Chieti Dept Neurosci Imaging & Clin Sci Pescara Italy Next2U srl Pescara Italy

Research studies on social robotics and human-robot interaction have gained insights into factors that influence people's perceptions and behaviors towards robots. However, adults' perceptions of robots may differ significantly from those of infants. Consequently, extending this knowledge also to infants' attitudes toward robots is a growing field of research. Indeed, infant-robot interaction (IRI) is emerging as a critical and necessary area of research as robots are increasingly used in social environments, such as caring for infants with all types of disabilities, companionship, and education. Although studies have been conducted on the ability of robots to positively engage infants, little is known about the infants' affective state when interacting with a robot. In this systematic review, technologies for infant affective state recognition relevant to IRI applications are presented and surveyed. Indeed, adapting techniques currently employed for infant's emotion recognition to the field of IRI results to be a complex task, since it requires timely response while not interfering with the infant's behavior. Those aspects have a crucial impact on the selection of the emotion recognition techniques and the related metrics to be used for this purpose. Therefore, this review is intended to shed light on the advantages and the current research challenges of the infants' affective state recognition approaches in the IRI field, elucidates a roadmap for their use in forthcoming studies as well as potentially provide support to future developments of emotion-aware robots.

关键词： Infant-robot interaction Affective computing developmental robotics Affective states Psychophysiology

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Goal emulation and planning in perceptual space using learned affordances

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robotics AND AUTONOMOUS SYSTEMS 2011年第7-8期59卷 580-595页

作者： Ugur, Emre Oztop, Erhan Sahin, Erol Biol ICT Grp NICT Kyoto Japan ATR Cognit Mech Labs Kyoto Japan Middle E Tech Univ Dept Comp Engn KOVAN Res Lab TR-06531 Ankara Turkey Osaka Univ Sch Engn Sci Osaka Japan

In this paper, we show that through self-interaction and self-observation, an anthropomorphic robot equipped with a range camera can learn object affordances and use this knowledge for planning. In the first step of learning, the robot discovers commonalities in its action-effect experiences by discovering effect categories. Once the effect categories are discovered, in the second step, affordance predictors for each behavior are obtained by learning the mapping from the object features to the effect categories. After learning, the robot can make plans to achieve desired goals, emulate end states of demonstrated actions, monitor the plan execution and take corrective actions using the perceptual structures employed or discovered during learning. We argue that the learning system proposed shares crucial elements with the development of infants of 7-10 months age, who explore the environment and learn the dynamics of the objects through goal-free exploration. In addition, we discuss goal emulation and planning in relation to older infants with no symbolic inference capability and non-linguistic animals which utilize object affordances to make action plans. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Affordance developmental robotics Sensorimotor learning Manipulation Perception Cognitive robotics

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A developmental approach to robotic pointing via human-robot interaction

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INFORMATION SCIENCES 2014年 283卷 288-303页

作者： Chao, Fei Wang, Zhengshuai Shang, Changjing Meng, Qinggang Jiang, Min Zhou, Changle Shen, Qiang Xiamen Univ Dept Cognit Sci Fujian Prov Key Lab Brain Like Intelligent Syst Xiamen 361005 Peoples R China Aberystwyth Univ Inst Math Phys & Comp Sci Dept Comp Sci Aberystwyth SY23 3DB Dyfed Wales Loughborough Univ Technol Dept Comp Sci Loughborough LE11 3TU Leics England

The ability of pointing is recognised as an essential skill of a robot in its communication and social interaction. This paper introduces a developmental learning approach to robotic pointing, by exploiting the interactions between a human and a robot. The approach is inspired through observing the process of human infant development. It works by first applying a reinforcement learning algorithm to guide the robot to create attempt movements towards a salient object that is out of the robot's initial reachable space. Through such movements, a human demonstrator is able to understand the robot desires to touch the target and consequently, to assist the robot to eventually reach the object successfully. The human robot interaction helps establish the understanding of pointing gestures in the perception of both the human and the robot. From this, the robot can collect the successful pointing gestures in an effort to learn how to interact with humans. developmental constraints are utilised to drive the entire learning procedure. The work is supported by experimental evaluation, demonstrating that the proposed approach can lead the robot to gradually gain the desirable pointing ability. It also allows that the resulting robot system exhibits similar developmental progress and features as with human infants. (C) 2014 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://***/licenses/by/3.0/).

关键词： developmental robotics Robotic pointing Human robot interaction

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Grounding the experience of a visual field through sensorimotor contingencies

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NEUROCOMPUTING 2017年 268卷 142-152页

作者： Laflaquiere, Alban SoftBank Robot Europe AI Lab 43 Rue Col Pierre Avia F-75015 Paris France

Artificial perception is traditionally handled by hand-designing task specific algorithms. However, a truly autonomous robot should develop perceptive abilities on its own, by interacting with its environment, and adapting to new situations. The sensorimotor contingencies theory proposes to ground the development of those perceptive abilities in the way the agent can actively transform its sensory inputs. We propose a sensorimotor approach, inspired by this theory, in which the agent explores the world and discovers its properties by capturing the sensorimotor regularities they induce. This work presents an application of this approach to the discovery of a so-called visual field as the set of regularities that a visual sensor imposes on a naive agent's experience. A formalism is proposed to describe how those regularities can be captured in a sensorimotor predictive model. Finally, the approach is evaluated on a simulated system coarsely inspired from the human retina. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Autonomous systems developmental robotics Sensorimotor contingencies Predictive processing Sensorimotor learning Human-like vision

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Robots That Say "No" Affective Symbol Grounding and the Case of Intent Interpretations

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IEEE TRANSACTIONS ON COGNITIVE AND developmental SYSTEMS 2018年第3期10卷 530-544页

作者： Foerster, Frank Saunders, Joe Nehaniv, Chrystopher L. Univ Hertfordshire Adapt Syst Res Grp Hatfield AL10 9AB Herts England

Modern theories on early child language acquisition tend to focus on referential words, mostly nouns, labeling concrete objects, or physical properties. In this experimental proof-of-concept study, we show how nonreferential negation words, typically belonging to a child's first ten words, may be acquired. A child-like humanoid robot is deployed in speech-wise unconstrained interaction with naive human participants. In agreement with psycholinguistic observations, we corroborate the hypothesis that affect plays a pivotal role in the socially distributed acquisition process where the adept conversation partner provides linguistic interpretations of the affective displays of the less adept speaker. Negation words are prosodically salient within intent interpretations that are triggered by the learner's display of affect. From there they can be picked up and used by the budding language learner which may involve the grounding of these words in the very affective states that triggered them in the first place. The pragmatic analysis of the robot's linguistic performance indicates that the correct timing of negative utterances is essential for the listener to infer the meaning of otherwise ambiguous negative utterances. In order to assess the robot's performance thoroughly comparative data from psycholinguistic studies of parent-child dyads is needed highlighting the need for further interdisciplinary work.

关键词： developmental robotics human-robot interaction language acquisition negation pragmatics psycholinguistics social robotics

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Interplay of Rhythmic and Discrete Manipulation Movements During Development: A Policy-Search Reinforcement-Learning Robot Model

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IEEE TRANSACTIONS ON COGNITIVE AND developmental SYSTEMS 2016年第3期8卷 152-170页

作者： Meola, Valentina Cristina Caligiore, Daniele Sperati, Valerio Zollo, Loredana Ciancio, Anna Lisa Taffoni, Fabrizio Guglielmelli, Eugenio Baldassarre, Gianluca Univ Campus Biomed Roma Dept Engn Unit Biomed Robot & Biomicrosyst I-00128 Rome Italy Consiglio Nazl Ric LOCEN ISTC CNR Ist Sci & Tecnol Cogniz Lab Computat Embodied Neurosci I-00128 Rome Italy

The flexibility of human motor behavior strongly relies on rhythmic and discrete movements. developmental psychology has shown how these movements closely interplay during development, but the dynamics of that are largely unknown and we currently lack computational models suitable to investigate such interaction. This work initially presents an analysis of the problem from a computational and empirical perspective and then proposes a novel computational model to start to investigate it. The model is based on a movement primitive capable of producing both rhythmic and end-point discrete movements, and on a policy search reinforcement learning algorithm capable of mimicking trial-and-error learning processes underlying development and efficient enough to work on real robots. The model is tested with hand manipulation tasks ("touching," "tapping," and "rotating" an object). The results show how the system progressively shapes the initial rhythmic exploration into refined rhythmic or discrete movements depending on the task demand. The tests on the real robot also show how the system exploits the specific hand-object physical properties, some possibly shared with developing infants, to find effective solutions to the tasks. The results show that the model represents a useful tool to investigate the interplay of rhythmic and discrete movements during development.

关键词： Central patter generators developmental robotics iCub robot hand motor babbling motor system and development policy search reinforcement learning rhythmic and discrete movement primitives

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A Behavior-Grounded Approach to Forming Object Categories: Separating Containers From Noncontainers

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IEEE TRANSACTIONS ON AUTONOMOUS MENTAL DEVELOPMENT 2012年第1期4卷 54-69页

作者： Griffith, Shane Sinapov, Jivko Sukhoy, Vladimir Stoytchev, Alexander Iowa State Univ Dept Elect & Comp Engn Dev Robot Lab Ames IA 50011 USA

This paper introduces a framework that allows a robot to form a single behavior-grounded object categorization after it uses multiple exploratory behaviors to interact with objects and multiple sensory modalities to detect the outcomes that each behavior produces. Our robot observed acoustic and visual outcomes from six different exploratory behaviors performed on 20 objects (containers and noncontainers). Its task was to learn 12 different object categorizations (one for each behavior-modality combination), and then to unify these categorizations into a single one. In the end, the object categorization acquired by the robot matched closely the object labels provided by a human. In addition, the robot acquired a visual model of containers and noncontainers based on its unified categorization, which it used to label correctly 29 out of 30 novel objects.

关键词： Artificial intelligence developmental robotics intelligent robots learning systems object categorization robots

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Robotic vocabulary building using extension inference and implicit contrast

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ARTIFICIAL INTELLIGENCE 2009年第1期173卷 145-166页

作者： Gold, Kevin Doniec, Marek Crick, Christopher Scassellati, Brian Yale Univ Dept Comp Sci New Haven CT 06520 USA

TWIG ("Transportable Word Intension Generator") is a system that allows a robot to learn compositional meanings for new words that are grounded in its sensory capabilities. The system is novel in its use of logical semantics to infer which entities in the environment are the referents (extensions) of unfamiliar words;its ability to learn the meanings of cleictic ("1," "this") pronouns in a real sensory environment;its use of decision trees to implicitly contrast new word definitions with existing ones, thereby creating more complex definitions than if each word were treated as a separate learning problem;and its ability to use words learned in an unsupervised manner in complete grammatical sentences for production, comprehension, or referent inference. In an experiment with a physically embodied robot, TWIG learns grounded meanings for the words "I" and "you," learns that "this" and "that" refer to objects of varying proximity, that "he" is someone talked about in the third person, and that "above" and "below" refer to height differences between objects. Follow-up experiments demonstrate the system's ability to learn different conjugations of "to be";show that removing either the extension inference or implicit contrast components of the system results in worse definitions;and demonstrate how decision trees can be used to model shifts in meaning based on context in the case of color words. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Word learning Context developmental robotics Robot Decision trees Symbol grounding TWIG Pronouns Autonomous mental development Inference

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Forming the Concept of Direction developmentally

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IEEE TRANSACTIONS ON COGNITIVE AND developmental SYSTEMS 2020年第4期12卷 759-773页

作者： Luo, Dingsheng Nie, Mengxi Wei, Yaoyao Hu, Fan Wu, Xihong Peking Univ Key Lab Machine Percept Minist EducSch Elect Engn & Comp Sci Speech & Hearing Res CtrDept Machine Intelligenc Beijing 100871 Peoples R China

The concept of direction is critical for a robot to establish its abilities in spatial perception. This article addresses the issue of how a robot developmentally forms the concept of direction. We propose a novel framework, based on the related mechanisms of humans, in which motion cues are employed instead of other commonly used sensing means like vision or audition. Using motion cues is actually one of the most important ways for humans to acquire concepts. This leads to advantages in two ways: 1) models based on motion cues are usually more convenient for a robot's control of motion and 2) multimodal perceptual cues can complement each other and result in improved robustness. The framework behind our methodology lies in developmentally forming the concept of direction in two successive phases: 1) the robot acquires the perceptual representation of its motion by motor babbling and 2) the corresponding higher level features are gradually captured, based on which the conceptual representation is obtained and then the concept of direction is formed. The proposed framework and the corresponding models are evaluated with a PKU-HR5.0II humanoid robot. The results show that the robot can successfully form the concept of direction in a manner similar to that of humans.

关键词： Robot sensing systems Visualization Task analysis Cognition Neural networks Arm motion cognitive development concept formation concept of direction conceptual representation developmental robotics humanoid robot internal model perceptual representation proprioception

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