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Journal articles on the topic 'Developmental robotics'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Lungarella, Max. "Developmental robotics." Scholarpedia 2, no. 8 (2007): 3104. http://dx.doi.org/10.4249/scholarpedia.3104.

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Gonzalo de Diego, Blanca, Alexandra González Aguña, Marta Fernández Batalla, et al. "Competencies in the Robotics of Care for Nursing Robotics: A Scoping Review." Healthcare 12, no. 6 (2024): 617. http://dx.doi.org/10.3390/healthcare12060617.

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In parallel with the development and design of different technological advances, competencies in nursing have advanced. With the development of robotics, it is expected that nursing robotic competencies will also increase. The aim of this study is to review the competencies in nursing robotics. A review was conducted between January 2017 and December 2023. The search strategy was carried out in the MEDLINE database (through PubMed). This review explores the developmental competencies in nursing robotics and informatics. The data extraction in this review included an intentional search for comp

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Lungarella, Max, Giorgio Metta, Rolf Pfeifer, and Giulio Sandini. "Developmental robotics: a survey." Connection Science 15, no. 4 (2003): 151–90. http://dx.doi.org/10.1080/09540090310001655110.

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Meeden, Lisa A., and Douglas S. Blank. "Introduction to developmental robotics." Connection Science 18, no. 2 (2006): 93–96. http://dx.doi.org/10.1080/09540090600806631.

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Jin, Yaochu, and Yan Meng. "Morphogenetic Robotics: An Emerging New Field in Developmental Robotics." IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 41, no. 2 (2011): 145–60. http://dx.doi.org/10.1109/tsmcc.2010.2057424.

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Asada, Minoru. "Creation of Future Robotics by Revisiting Cognitive Developmental Robotics." Journal of the Robotics Society of Japan 41, no. 5 (2023): 419–26. http://dx.doi.org/10.7210/jrsj.41.419.

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Asada, M., K. Hosoda, Y. Kuniyoshi, et al. "Cognitive Developmental Robotics: A Survey." IEEE Transactions on Autonomous Mental Development 1, no. 1 (2009): 12–34. http://dx.doi.org/10.1109/tamd.2009.2021702.

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Elliott, Terry, and Nigel R. Shadbolt. "Developmental robotics: manifesto and application." Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences 361, no. 1811 (2003): 2187–206. http://dx.doi.org/10.1098/rsta.2003.1250.

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WENG, JUYANG. "DEVELOPMENTAL ROBOTICS: THEORY AND EXPERIMENTS." International Journal of Humanoid Robotics 01, no. 02 (2004): 199–236. http://dx.doi.org/10.1142/s0219843604000149.

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A hand-designed internal representation of the world cannot deal with unknown or uncontrolled environments. Motivated by human cognitive and behavioral development, this paper presents a theory, an architecture, and some experimental results for developmental robotics. By a developmental robot, we mean that the robot generates its "brain" (or "central nervous system," including the information processor and controller) through online, real-time interactions with its environment (including humans). A new Self-Aware Self-Effecting (SASE) agent concept is proposed, based on our SAIL and Dav devel

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Johansson, Birger, Trond A. Tjøstheim, and Christian Balkenius. "Epi: An open humanoid platform for developmental robotics." International Journal of Advanced Robotic Systems 17, no. 2 (2020): 172988142091149. http://dx.doi.org/10.1177/1729881420911498.

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Epi is a humanoid robot developed by Lund University Cognitive Science Robotics Group. It was designed to be used in experiments in developmental robotics and has proportions to give a childlike impression while still being decidedly robotic. The robot head has two degrees of freedom in the neck and each eye can independently move laterally. There is a camera in each eye to make stereovision possible. The arms are designed to resemble those of a human. Each arm has five degrees of freedom, three in the shoulder, one in the elbow and one in the wrist. The hands have four movable fingers and a s

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Stoytchev, A. "Some Basic Principles of Developmental Robotics." IEEE Transactions on Autonomous Mental Development 1, no. 2 (2009): 122–30. http://dx.doi.org/10.1109/tamd.2009.2029989.

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Lee, Mark H., Qinggang Meng, and Fei Chao. "Staged Competence Learning in Developmental Robotics." Adaptive Behavior 15, no. 3 (2007): 241–55. http://dx.doi.org/10.1177/1059712307082085.

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ISHIGURO, HIROSHI, TAKASHI MINATO, YUICHIRO YOSHIKAWA, and MINORU ASADA. "HUMANOID PLATFORMS FOR COGNITIVE DEVELOPMENTAL ROBOTICS." International Journal of Humanoid Robotics 08, no. 03 (2011): 391–418. http://dx.doi.org/10.1142/s0219843611002514.

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One of the most promising approaches to understand human cognitive and developmental mechanisms is a synthetic approach using humanoid robots; an approach to understand the human cognitive functions by realizing them with the robots. Humans are so complicated and it is difficult to mimic the well-developed human by robotic technologies. Therefore, it is necessary to understand how humans develop the complicated functions during the developmental process. We may be able to develop infant functions and make them evolve by tracing the human developmental process. This new study requires robot pla

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Pandey, Amit Kumar, Rachid Alami, and Kazuhiko Kawamura. "Developmental Social Robotics: An Applied Perspective." International Journal of Social Robotics 7, no. 4 (2015): 417–20. http://dx.doi.org/10.1007/s12369-015-0312-0.

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Xu, Bo, Huaqing Min, and Fangxiong Xiao. "A brief overview of evolutionary developmental robotics." Industrial Robot: An International Journal 41, no. 6 (2014): 527–33. http://dx.doi.org/10.1108/ir-04-2014-0324.

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Purpose – This article aims to provide a brief overview of the field now known as “evolutionary developmental robotics (evo-devo-robo)”, which is based on the concept and principles of evolutionary and development principles such as evolutionary developmental psychology, evolutionary developmental biology (evo-devo) and evolutionary cognitive neuroscience. Design/methodology/approach – Evo-devo-robo is a new field bringing together developmental robotics and evolutionary robotics to form a new research area. Basic concepts and the origins of the field are described, and then some basic princip

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Salah, Albert Ali, Pierre-Yves Oudeyer, Cetin Mericli, and Javier Ruiz-del-Solar. "Guest Editorial Behavior Understanding and Developmental Robotics." IEEE Transactions on Autonomous Mental Development 6, no. 2 (2014): 77–79. http://dx.doi.org/10.1109/tamd.2014.2328731.

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Meeden, Lisa A. "Angelo Cangelosi and Matthew Schlesinger: Developmental robotics." Genetic Programming and Evolvable Machines 16, no. 3 (2015): 397–98. http://dx.doi.org/10.1007/s10710-015-9246-4.

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Min, Huaqing, Chang'an Yi, Ronghua Luo, Jinhui Zhu, and Sheng Bi. "Affordance Research in Developmental Robotics: A Survey." IEEE Transactions on Cognitive and Developmental Systems 8, no. 4 (2016): 237–55. http://dx.doi.org/10.1109/tcds.2016.2614992.

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Kumar, Suresh, and Patricia Sha. "Human Brain inspired Artificial Intelligence & Developmental Robotics: A Review." Sukkur IBA Journal of Computing and Mathematical Sciences 1, no. 1 (2017): 43. http://dx.doi.org/10.30537/sjcms.v1i1.6.

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Along with the developments in the field of the robotics, fascinating contributions and developments can be seen in the field of Artificial intelligence (AI). In this paper we will discuss about the developments is the field of artificial intelligence focusing learning algorithms inspired from the field of Biology, particularly large scale brain simulations, and developmental Psychology. We will focus on the emergence of the Developmental robotics and its significance in the field of AI.

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Liang, Wei, Hao Liu, Kunyang Wang, Zhihui Qian, Luquan Ren, and Lei Ren. "Comparative study of robotic artificial actuators and biological muscle." Advances in Mechanical Engineering 12, no. 6 (2020): 168781402093340. http://dx.doi.org/10.1177/1687814020933409.

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Biological muscles exhibit a high level of integration, in which actuators, sensors and transmission elements can be included in one component. Artificial muscles or actuators refer to intelligent stimuli-responsive materials that could reversibly deform with the trigger of various external stimuli. These materials, which have attracted tremendous attention, produce natural muscle-like actuation performance and show promising applications in robotics. After an introduction of various actuator technologies that contribute to robotic applications, a comparative analysis of the main actuation par

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Cangelosi, Angelo, and Matthew Schlesinger. "From Babies to Robots: The Contribution of Developmental Robotics to Developmental Psychology." Child Development Perspectives 12, no. 3 (2018): 183–88. http://dx.doi.org/10.1111/cdep.12282.

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Asada, Minoru. "Towards Artificial Empathy based on Affective Developmental Robotics." Journal of the Robotics Society of Japan 32, no. 8 (2014): 666–77. http://dx.doi.org/10.7210/jrsj.32.666.

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Conti, Daniela, Santo Di Nuovo, and Angelo Cangelosi. "Il contributo metodologico della Developmental Robotics alla psicologia." RICERCHE DI PSICOLOGIA, no. 2 (July 2018): 221–39. http://dx.doi.org/10.3280/rip2018-002002.

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Kelley, Troy D., and Daniel N. Cassenti. "Theoretical explorations of cognitive robotics using developmental psychology." New Ideas in Psychology 29, no. 3 (2011): 228–34. http://dx.doi.org/10.1016/j.newideapsych.2009.07.002.

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Merrick, Kathryn. "Value systems for developmental cognitive robotics: A survey." Cognitive Systems Research 41 (March 2017): 38–55. http://dx.doi.org/10.1016/j.cogsys.2016.08.001.

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researcher. "ADAPTIVE MORPHOGENETIC FRAMEWORKS FOR SOFT ROBOTIC MANIPULATORS IN BIOLOGICALLY ACTIVE ENVIRONMENTS INTEGRATING BIO-INSPIRED CONTROL WITH DYNAMIC STRUCTURAL RECONFIGURATION." International Journal of Robotics and Process Automation (IJRPA) 6, no. 1 (2025): 1–6. https://doi.org/10.5281/zenodo.15182069.

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<em>This study explores an integrative morphogenetic framework that enables soft robotic manipulators to adaptively respond to dynamic and biologically active environments. Inspired by developmental biology and embodied intelligence, the research aims to unify structural flexibility with decentralized, bio-inspired control mechanisms. By synthesizing prior advancements in morphogenetic robotics and bio-mimetic actuation, we propose a responsive architecture capable of dynamic reconfiguration and environmental interaction. Applications range from targeted drug delivery to environmental sensing

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Tadahiro, Taniguchi, Murata Shingo, Suzuki Masahiro, et al. "World models and predictive coding for cognitive and developmental robotics: frontiers and challenges." Advanced Robotics 37, no. 13 (2023): 780–806. https://doi.org/10.1080/01691864.2023.2225232.

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Creating autonomous robots that can actively explore the environment, acquire knowledge and learn skills continuously is the ultimate achievement envisioned in cognitive and developmental robotics. Importantly, if the aim is to create robots that can continuously develop through interactions with their environment, their learning processes should be based on interactions with their physical and social world in the manner of human learning and cognitive development. Based on this context, in this paper, we focus on the two concepts of <em>world models</em> and <em>predictive codi

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Wagner, Christopher R., Timothy Phillips, Serge Roux, and Joseph P. Corrigan. "Future Directions in Robotic Neurosurgery." Operative Neurosurgery 21, no. 4 (2021): 173–80. http://dx.doi.org/10.1093/ons/opab135.

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Abstract In this paper, we highlight promising technologies in each phase of a robotic neurosurgery operation, and identify key factors affecting how quickly these technologies will mature into products in the operating room. We focus on specific technology trends in image-guided cranial and spinal procedures, including advances in imaging, machine learning, robotics, and novel interfaces. For each technology, we discuss the required effort to overcome safety or implementation challenges, as well as identifying example regulatory approved products in related fields for comparison. The goal is

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Eppe, Manfred, Stefan Wermter, Verena V. Hafner, and Yukie Nagai. "Developmental Robotics and its Role Towards Artificial General Intelligence." KI - Künstliche Intelligenz 35, no. 1 (2021): 5–7. http://dx.doi.org/10.1007/s13218-021-00706-w.

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Jin, Yaochu, and Yan Meng. "Special Issue on Evolutionary and Developmental Robotics [Guest Editorial." IEEE Computational Intelligence Magazine 5, no. 3 (2010): 9. http://dx.doi.org/10.1109/mci.2010.937327.

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Morimoto, Jun, and Mitsuo Kawato. "Creating the brain and interacting with the brain: an integrated approach to understanding the brain." Journal of The Royal Society Interface 12, no. 104 (2015): 20141250. http://dx.doi.org/10.1098/rsif.2014.1250.

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In the past two decades, brain science and robotics have made gigantic advances in their own fields, and their interactions have generated several interdisciplinary research fields. First, in the ‘understanding the brain by creating the brain’ approach, computational neuroscience models have been applied to many robotics problems. Second, such brain-motivated fields as cognitive robotics and developmental robotics have emerged as interdisciplinary areas among robotics, neuroscience and cognitive science with special emphasis on humanoid robots. Third, in brain–machine interface research, a bra

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Vujovic, Vuk, Andre Rosendo, Luzius Brodbeck, and Fumiya Iida. "Evolutionary Developmental Robotics: Improving Morphology and Control of Physical Robots." Artificial Life 23, no. 2 (2017): 169–85. http://dx.doi.org/10.1162/artl_a_00228.

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Evolutionary algorithms have previously been applied to the design of morphology and control of robots. The design space for such tasks can be very complex, which can prevent evolution from efficiently discovering fit solutions. In this article we introduce an evolutionary-developmental (evo-devo) experiment with real-world robots. It allows robots to grow their leg size to simulate ontogenetic morphological changes, and this is the first time that such an experiment has been performed in the physical world. To test diverse robot morphologies, robot legs of variable shapes were generated durin

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Morse, Anthony F., and Angelo Cangelosi. "Why Are There Developmental Stages in Language Learning? A Developmental Robotics Model of Language Development." Cognitive Science 41 (September 28, 2016): 32–51. http://dx.doi.org/10.1111/cogs.12390.

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Kaplan, Frédéric, and Verena V. Hafner. "The challenges of joint attention." Epigenetic robotics 7, no. 2 (2006): 135–69. http://dx.doi.org/10.1075/is.7.2.04kap.

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This article discusses the concept of joint attention and the different skills underlying its development. Research in developmental psychology clearly states that the development of skills to understand, manipulate and coordinate attentional behavior plays a pivotal role for imitation, social cognition and the development of language. However, beside the fact that joint attention has recently received an increasing interest in the robotics community, existing models concentrate only on partial and isolated elements of these phenomena. In the line of Tomasello’s research, we argue that joint a

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Chatzopoulos, Avraam, Michail Kalogiannakis, Stamatis Papadakis, and Michail Papoutsidakis. "A Novel, Modular Robot for Educational Robotics Developed Using Action Research Evaluated on Technology Acceptance Model." Education Sciences 12, no. 4 (2022): 274. http://dx.doi.org/10.3390/educsci12040274.

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This research evaluates a novel, modular, open-source, and low-cost educational robotic platform in Educational Robotics and STEM Education. It is the sequel of an action research cycle on which the development of this robot is based. The impetus for the need to develop this came from the evaluation of qualitative and quantitative research data collected during an educational robotics event with significant participation of students in Athens, which showed an intense interest in students in participating in educational robotics activities, but—at the same time—recorded their low involvement du

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Vinanzi, Samuele, Massimiliano Patacchiola, Antonio Chella, and Angelo Cangelosi. "Would a robot trust you? Developmental robotics model of trust and theory of mind." Philosophical Transactions of the Royal Society B: Biological Sciences 374, no. 1771 (2019): 20180032. http://dx.doi.org/10.1098/rstb.2018.0032.

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Trust is a critical issue in human–robot interactions: as robotic systems gain complexity, it becomes crucial for them to be able to blend into our society by maximizing their acceptability and reliability. Various studies have examined how trust is attributed by people to robots, but fewer have investigated the opposite scenario, where a robot is the trustor and a human is the trustee. The ability for an agent to evaluate the trustworthiness of its sources of information is particularly useful in joint task situations where people and robots must collaborate to reach shared goals. We propose

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Dolenc, Nathan R., Robert Tai, and Douglas Williams. "Excessive Mentoring? An Apprenticeship Model on a Robotics Team." Journal of Research in STEM Education 6, no. 2 (2020): 91–114. http://dx.doi.org/10.51355/jstem.2020.81.

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Participation on a robotics team affords students the opportunity to learn science and engineering skills in a competition-based environment. Mentors on these robotics teams play important roles in helping students acquire these skills. This study used an apprenticeship learning theory to examine how mentors on one high school robotics team contributed to students attaining the knowledge associated with designing and building a robot for competition. How active of a role did mentors play on their competition-based robotics team? How did mentors and students together handle the challenges they

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Cangelosi, Angelo, Giorgio Metta, Gerhard Sagerer, et al. "Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics." IEEE Transactions on Autonomous Mental Development 2, no. 3 (2010): 167–95. http://dx.doi.org/10.1109/tamd.2010.2053034.

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Huelse, Martin, Sebastian McBride, James Law, and Mark Lee. "Integration of Active Vision and Reaching From a Developmental Robotics Perspective." IEEE Transactions on Autonomous Mental Development 2, no. 4 (2010): 355–67. http://dx.doi.org/10.1109/tamd.2010.2081667.

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Schmidhuber, Jürgen. "Developmental robotics, optimal artificial curiosity, creativity, music, and the fine arts." Connection Science 18, no. 2 (2006): 173–87. http://dx.doi.org/10.1080/09540090600768658.

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Prince, Christopher G., and Lakshmi J. Gogate. "Epigenetic Robotics: Behavioral Treatments and Potential New Models for Developmental Pediatrics." Pediatric Research 61, no. 4 (2007): 383–85. http://dx.doi.org/10.1203/pdr.0b013e3180459fdd.

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Hellström, Thomas. "The relevance of causation in robotics: A review, categorization, and analysis." Paladyn, Journal of Behavioral Robotics 12, no. 1 (2021): 238–55. http://dx.doi.org/10.1515/pjbr-2021-0017.

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Abstract In this article, we investigate the role of causal reasoning in robotics research. Inspired by a categorization of human causal cognition, we propose a categorization of robot causal cognition. For each category, we identify related earlier work in robotics and also connect to research in other sciences. While the proposed categories mainly cover the sense–plan–act level of robotics, we also identify a number of higher-level aspects and areas of robotics research where causation plays an important role, for example, understandability, machine ethics, and robotics research methodology.

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SHULTZ, THOMAS R., FRANÇOIS RIVEST, LÁSZLÓ EGRI, JEAN-PHILIPPE THIVIERGE, and FRÉDÉRIC DANDURAND. "COULD KNOWLEDGE-BASED NEURAL LEARNING BE USEFUL IN DEVELOPMENTAL ROBOTICS? THE CASE OF KBCC." International Journal of Humanoid Robotics 04, no. 02 (2007): 245–79. http://dx.doi.org/10.1142/s0219843607001035.

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The new field of developmental robotics faces the formidable challenge of implementing effective learning mechanisms in complex, dynamic environments. We make a case that knowledge-based learning algorithms might help to meet this challenge. A constructive neural learning algorithm, knowledge-based cascade-correlation (KBCC), autonomously recruits previously-learned networks in addition to the single hidden units recruited by ordinary cascade-correlation. This enables learning by analogy when adequate prior knowledge is available, learning by induction from examples when there is no relevant p

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Alnafjan, Abeer, Maha Alghamdi, Noura Alhakbani, and Yousef Al-Ohali. "Improving Imitation Skills in Children with Autism Spectrum Disorder Using the NAO Robot and a Human Action Recognition." Diagnostics 15, no. 1 (2024): 60. https://doi.org/10.3390/diagnostics15010060.

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Background/Objectives: Autism spectrum disorder (ASD) is a group of developmental disorders characterized by poor social skills, low motivation in activities, and a lack of interaction with others. Traditional intervention approaches typically require support under the direct supervision of well-trained professionals. However, teaching and training programs for children with ASD can also be enhanced by assistive technologies, artificial intelligence, and robotics. Methods: In this study, we examined whether robotics can improve the imitation skills of children with autism and support therapist

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Martínez Rojas, Ezequiel, Alejandro Valencia-Arias, Manuel Humberto Vásquez Coronado, et al. "Educational robotics for primary education: An analysis of research trends." Eurasia Journal of Mathematics, Science and Technology Education 21, no. 3 (2025): em2602. https://doi.org/10.29333/ejmste/16050.

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The present study explores the benefits and challenges of integrating technology into the early stages of education, including the use of robotics in primary education. While social and emotional learning has been widely studied in the Ibero-American context, there is a paucity of comprehensive studies examining the integration of robotics at this educational level. The present study aims to identify research trends through a bibliometric analysis conducted in accordance with the PRISMA statement, utilizing the Scopus and Web of Science databases. The results of this study highlight key develo

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Kuniyoshi, Yasuo. "Fusing autonomy and sociality via embodied emergence and development of behaviour and cognition from fetal period." Philosophical Transactions of the Royal Society B: Biological Sciences 374, no. 1771 (2019): 20180031. http://dx.doi.org/10.1098/rstb.2018.0031.

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Human-centred AI/Robotics are quickly becoming important. Their core claim is that AI systems or robots must be designed and work for the benefits of humans with no harm or uneasiness. It essentially requires the realization of autonomy, sociality and their fusion at all levels of system organization, even beyond programming or pre-training. The biologically inspired core principle of such a system is described as the emergence and development of embodied behaviour and cognition. The importance of embodiment, emergence and continuous autonomous development is explained in the context of develo

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Prieto, A., A. Romero, F. Bellas, R. Salgado, and R. J. Duro. "Introducing separable utility regions in a motivational engine for cognitive developmental robotics." Integrated Computer-Aided Engineering 26, no. 1 (2018): 3–20. http://dx.doi.org/10.3233/ica-180578.

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Amri, Saehful, Cucuk Wawan Budiyanto, Kristof Fenyvesi, Rosihan Ari Yuana, and Indah Widiastuti. "Educational Robotics: Evaluating the Role of Computational Thinking in Attaining 21st Century Skills." Open Education Studies 4, no. 1 (2022): 322–38. http://dx.doi.org/10.1515/edu-2022-0174.

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Abstract Educational Robotics (ER) has gained prominence in the literature on Computational Thinking (CT) because of its modularity, a feature that potentially facilitates the development of abstract thinking through complex robotic parts. The field of robotics encompasses the characteristics of technology, intelligence, embodiment, and interaction, and these characteristics can serve as means of instruction for CT. Essential 21st Century Skills include decomposition, pattern recognition, abstraction, and the use of algorithms; which are fundamental to effective problem-solving skills. Althoug

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Lee, Kangwoo, and Hyunseung Choo. "Constructing Perceptual Common Ground Between Human and Robot Through Joint Attention." International Journal of Humanoid Robotics 14, no. 03 (2017): 1750020. http://dx.doi.org/10.1142/s0219843617500207.

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Joint attention is a communicative activity that allows social partners to share perceptual experiences by jointly attending to an environmental object. Unlike the common approach towards joint attention, which is based on the developmental view in robotics, here it is conceptualized with a psychophysical paradigm known as cueing. The triadic interaction of joint attention is formalized as the conditional probability of an attentional response for a given target candidate derived from object features and a cue derived from a human partner's indication. A robotic system to which the joint atten

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Yao, Lili, Rick van de Zedde, and George Kowalchuk. "Recent developments and potential of robotics in plant eco-phenotyping." Emerging Topics in Life Sciences 5, no. 2 (2021): 289–300. http://dx.doi.org/10.1042/etls20200275.

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Automated acquisition of plant eco-phenotypic information can serve as a decision-making basis for precision agricultural management and can also provide detailed insights into plant growth status, pest management, water and fertilizer management for plant breeders and plant physiologists. Because the microscopic components and macroscopic morphology of plants will be affected by the ecological environment, research on plant eco-phenotyping is more meaningful than the study of single-plant phenotyping. To achieve high-throughput acquisition of phenotyping information, the combination of high-p

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