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Introducing separable utility regions in a motivational engine for cognitive developmental robotics

INTEGRATED COMPUTER-AIDED ENGINEERING 2019年第1期26卷 3-20页

作者： Prieto, A. Romero, A. Bellas, F. Salgado, R. Duro, R. J. Univ A Coruna Integrated Grp Engn Res La Coruna Spain

cognitive developmental robotics relies on lifelong open-ended learning processes, where mechanisms are needed to allow the robot to self-discover and self-select goals as well as to self-define its state space evaluation with regards to them. Thus, this paper addresses the problem of finding and using goals in continuous state spaces and automatically obtaining sub-goal hierarchies that allow autonomous development. In particular, the main purpose of this paper is to propose a new approach to the creation of utility models based on the concept of separable utility regions (SURs), which reduce the complexity of standard value function like utility models. These regions exhibit a correlation between the expected utility and the response of one sensor of the robot. Once they are discovered, the evaluation of the candidate states is only based on the changes of one sensor, which provides a strong independence from noise or dynamism in the utility models. A non-static variation of the classical collect-a-ball scenario and a robot gathering problem were used to test this approach in simulation and on real robots in order to identify goals and sub-goals in an autonomous way. The results confirm the good response of the method as a highly promising approach towards autonomous learning of continuous domains in cognitive robotics.

关键词： Motivational system extrinsic motivation cognitive developmental robotics goal identification

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Combined Computational Systems Biology and Computational Neuroscience Approaches Help Develop of Future "cognitive developmental robotics"

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FRONTIERS IN NEUROrobotics 2017年第NOV期11卷 63页

作者： Faghihi, Faramarz Moustafa, Ahmed A. Shahid Beheshti Univ Inst Cognit & Brain Sci Dept Cognit Modeling Tehran Iran Western Sydney Univ Sch Social Sci & Psychol Sydney NSW Australia Western Sydney Univ Marcs Inst Brain & Behav Sydney NSW Australia

[...]we suggest that novel integrative approaches to develop SNNs like which integrate electrophysiological features of neural systems and algorithms from both systems biology and computational neuroscience algorithms, will allow for the development of intelligent cognitive systems (Figure 1). Specially, we claim that computational systems biology based algorithms for memory and learning play fundamental roles in progress toward implementing cognitive functions in CDR. [...]using these algorithms in CDR systems may be used in experimental studies to design and test experiments. [...]to implement molecular mechanisms of synaptic events one can avoid information on channel expression and their biophysical activities by using simplified neuron models like Leaky Integrate Fire neuron model (LIF) that is widely used in spiking neural networks (Izhikevich, 2003). According to a systems biology based hypothesis, retroaxonal signaling transfer

关键词： neural networks (computer) spiking neurons intelligent systems cognitive developmental robotics computational systems biology

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developmental Learning of Value Functions in a Motivational System for cognitive robotics

Developmental Learning of Value Functions in a Motivational ...

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International Joint Conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Romero, Alejandro Bellas, Francisco Prieto, Abraham Duro, Richard J. Univ A Coruna GII CITIC Res Ctr La Coruna Spain

ISBN: (纸本)9781728169262

Motivation is quite an important topic when addressing continual open-ended learning processes in autonomous robots. The three main issues that need to be considered are, firstly, how does a designer define what the robot strives for in a manner that is independent from any particular domain it may find itself in. Secondly, once that robot is in a domain, how does it go about finding and relating goals in that particular domain on its own. Finally, the third issue is, once a goal is found, how does a robot establish a representation, usually in the form of a Value Function, that will allow it to exploit that goal. This paper deals with the third issue in the framework of the motivational engine we have designed for cognitive architectures. It addresses the problem of efficiently and appropriately learning complex Value Functions starting from intrinsically motivated traces of valuated robot actions that are often ambiguous and multivalued. To this end, a developmental learning mechanism is proposed that relies on the concurrent application of a real time ANN learning procedure over the traces of the valuated robot actions, and a simpler sensor correlation-based approach to allow for the production of better configured data traces for the learning process. The mechanism is analyzed and discussed over an experiment considering a real Baxter robot.

关键词： motivation cognitive developmental robotics open-ended learning valuefunction learning

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Stories of QRIO and PINO, and Beyond: Lessons Learned from Small Humanoid Projects From R&D to Business

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INTERNATIONAL JOURNAL OF HUMANOID robotics 2024年第1期21卷

作者： Fujita, Masahiro Kawanami, Yasunori Miyazawa, Kiyokazu Kinoshita, Masaya Sawai, Kunihito Yamasaki, Fuminori Matsui, Tatsuya Endo, Ken Ishiguro, Shu Kitano, Hiroaki Sony Grp Corp Technol Infrastruct Ctr AI Technol Div 2-10-1 Osaki Shinagawa Tokyo Japan Sony Grp Corp Creat Ctr Creat Platform 1-7-1 KonanMinato Ku Tokyo Japan iXs Co Ltd 7-7 Shin Kawasaki Kawasaki Kanagawa Japan Flower Robot Inc J House 301 Tokyo Japan Sony Comp Sci Labs Inc 3-14-13 Higashigotanda Tokyo Japan XiBorg Inc 4026-34-3 Jingumae Tokyo Japan Chiba Inst Technol Future Robot Technol Ctr 2-17-1 Tsudanuma Narashino Chiba 2750016 Japan Sony Grp Corp 1-7-1 KonanMinato Ku Tokyo Japan Okinawa Inst Sci & Technol Grad Sch 1919-1 Tancha Okinawa 9040495 Japan

In 1997, Sony announced AIBO, a fully autonomous small quadruped robot for home entertainment, and in 1999 the company began selling it as a consumer product. Soon after development, two small humanoid robots were announced. One was QRIO by Sony, which is about 60cm height body with dynamical bipedal walking and whole-body cooperative control. The other was PINO by the ERATO Kitano Symbiotic Systems Project, which is about 70cm height body, Open HW/SW with inexpensive off-the-shelf component. In this paper, we revisit the two humanoids and nearly 20-year-old technologies, and discuss what were done 20 years ago, what have been achieved and what challenges are ahead.

关键词： QRIO PINO zero moment point whole-body motion control force control series-elastic actuator genetic algorithm behavior control architecture cognitive developmental robotics OPEN-R OpenPINO Digital-Twin prosthesis intelligence dynamics multi-modal large language model

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Learning in Social Interaction: Perspectives from Psychology and robotics

Learning in Social Interaction: Perspectives from Psychology...

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IEEE International Conference on Development and Learning (ICDL)

作者： Kirtay, Murat Chevalere, Johann Lazarides, Rebecca Hafner, Verena V. Humboldt Univ Dept Comp Sci Adapt Syst Grp Berlin Germany Univ Potsdam Dept Educ Sci Potsdam Germany

ISBN: (纸本)9781728162423

Robots are being increasingly deployed in daily life situations. To achieve human-like, authentic interactions with their biological counterparts, endowing robots with learning mechanisms is critical, especially in social contexts. To contribute to this aim, in this paper we introduce cognitive learning mechanisms that are presented in psychology and describe how these theoretical approaches inform current research in robotics. On the one hand, from a psychology perspective, we focus on the learning components of Piaget's constructivism and Vygotsky's socio-constructivism. On the other hand, from a robotics perspective, we discuss the implementations and limitations of the applications of these psychological theories in cognitive-developmental and social robotics research. Finally, based on the insights from psychological theories and robotics studies, we propose a conceptual model of a cognitive architecture that hosts components from psychological theories aimed at enabling social robots to learn from biological agents. Thus, a comprehensive goal of the described architecture is to guide social learning processes that can be enhanced in robots that result in their ability to perform complex adaptive behaviors that are observed in their biological counterparts.

关键词： Piaget Vygotsky social robotics cognitive developmental robotics cognitive architectures

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Exploring Deep Models for Comprehension of Deictic Gesture-Word Combinations in cognitive robotics

Exploring Deep Models for Comprehension of Deictic Gesture-W...

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International Joint Conference on Neural Networks (IJCNN)

作者： Pizzuto, Gabriella Cangelosi, Angelo Univ Manchester Sch Comp Sci Manchester Lancs England

ISBN: (纸本)9781728119854

In the early stages of infant development, gestures and speech are integrated during language acquisition. Such a natural combination is therefore a desirable, yet challenging, goal for fluid human-robot interaction. To achieve this, we propose a multimodal deep learning architecture, for comprehension of complementary gesture-word combinations, implemented on an iCub humanoid robot. This enables human-assisted language learning, with interactions like pointing at a cup and labelling it with a vocal utterance. We evaluate various depths of the Mask Regional Convolutional Neural Network (for object and wrist detection) and the Residual Network (for gesture classification). Validation is carried out with two deictic gestures across ten real-world objects on frames recorded directly from the iCub's cameras. Results further strengthen the potential of gesture-word combinations for robot language acquisition.

关键词： cognitive developmental robotics embodied language acquisition

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Editorial: Body Representations, Peripersonal Space, and the Self: Humans, Animals, Robots

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FRONTIERS IN NEUROrobotics 2020年 14卷 35页

作者： Hoffmann, Matej Lanillos, Pablo Jamone, Lorenzo Pitti, Alex Somogyi, Eszter Czech Tech Univ Dept Cybernet Fac Elect Engn Prague Czech Republic Radboud Univ Nijmegen Donders Inst Brain Cognit & Behav Nijmegen Netherlands Queen Mary Univ London ARQ Adv Robot Queen Mary Sch Elect Engn & Comp Sci London England CY Cergy Paris Univ Lab ETIS ENSEA CNRSUMR8051 Cergy Pontoise France Univ Portsmouth Dept Psychol Ctr Situated Act & Commun Portsmouth Hants England

[...]the way humans represent their bodies and the space around them provide a proxy for what they expect from a robot collaborator. [...]a good understanding of these phenomena is the basis for safe and natural human-robot interaction, as studied by Schürmann et al. and also Heijnen et al.. [...]self-unity, as Rochat proposes, could represent important grounding information for artificial learning systems, allowing them to learn rapidly like human children do. In their EEG study, Drew et al. show that the infant brain registers correspondences between infants' own bodies and the bodies of others. [...]responses to tactile stimulation to the hand or the foot were modulated by simultaneous vision of the corresponding or non-corresponding effector of another person being touched. 2.

关键词： body representations peripersonal space self neurorobotics cognitive developmental robotics body schema body image development of body representations

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Influence of Locomotion Experience for Distance Estimation in Infants: A Computational Model

Influence of Locomotion Experience for Distance Estimation i...

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2nd IEEE Colombian Conference on Computational Intelligence (ColCACI)

作者： Jaramillo, Liz Velez, Adrian Arango, Jaime Univ Nacl Colombia Dept Elect & Elect Engn Manizales Colombia

ISBN: (纸本)9781728116143

Self-locomotion experience of infants has been argued to improve perception of distance, as visual attention is drawn to previously undetected or ignored depth specifying information. We present a computational model to evaluate how does self-locomotion experience influences the estimation of distance in infants. The model assigns an estimated distance label to salient objects in the scene, through a Binocular Neural Network (BNN) that computes binocular disparities. Emphasizing on key aspects of locomotion experience, two BNN are trained, one for non-locomotor infants and one for locomotor infants. The validation and test stages of the process show a significant improvement on the distance estimation task for the BNN trained with locomotor experience. This result is added to previous evidence which supports that locomotion in infants is an important step in cognitive development.

关键词： cognitive developmental robotics visual attention locomotor experience binocular neural network

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Bootstrapping Autonomous Skill Learning in the MDB cognitive Architecture 1

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8th International Work-Conference on the Interplay Between Natural and Artificial Computation (IWINAC)

作者： Romero, Alejandro Bellas, Francisco Becerra, Jose A. Duro, Richard J. Univ A Coruna Integrated Grp Engn Res La Coruna Spain

ISBN: (数字)9783030195915

ISBN: (纸本)9783030195915;9783030195908

This paper is concerned with motivation in autonomous robots. In particular we focus on the basic structure that is necessary for bootstrapping the initial stages of multiple skill learning within the motivational engine of the MDB cognitive architecture. To this end, taking inspiration from a series of computational models of the use of motivations in infants, we propose an approach that leverages two types of cognitive motivations: exploratory and proficiency based. The latter modulated by the concept of interestingness. We postulate that these make up the minimum set of motivational components required to initiate the unrewarded learning of a skill toolbox that may later be used in order to achieve operational goals. The approach is illustrated through an experiment with a real robot that is learning skills in a real environment.

关键词： cognitive developmental robotics Motivational system Skill learning Open-ended learning

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Constructing a Fundamental developmental Drawing Learning Model Using an Arm Robot 6

Constructing a Fundamental Developmental Drawing Learning Mo...

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6th IEEE International Conference on Cloud Computing and Intelligence Systems (IEEE CCIS)

作者： Yasumura, Ryosuke Nishide, Shun Kang, Xin Ren, Fuji Tokushima Univ Tokushima 7708506 Japan

ISBN: (数字)9781728138633

ISBN: (纸本)9781728138633

The goal of this study is to construct a developmental drawing learning model using an arm robot based on the five stages of human drawing development model proposed by Luquet. In this paper, we particularly deal with (1) Scribbling and (3) Failed Realism within the five stages. The proposed model is constructed using Multiple Timescale Recurrent Neural Network (MTRNN), which is a type of recurrent neural network model, as the learning model. In scribbling (body babbling), the model learns the relationship between the pen point coordinate series and the joint angle series by training using random drawing motion of the robot. After model training, an imitation drawing system was constructed based on failed realism In this stage, the robot recognizes the pen point coordinate series of a drawn figure shown by a human subject to associate the joint angle series of the robot motion that will possibly accomplish the drawing of the figure. Experiments were conducted with an actual arm robot to confirm the validity of the robot to imitate drawing of the basic figures.

关键词： cognitive developmental robotics Recurrent neural network Imitation learning

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