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Готові списки джерел за темами / Robotics and neuroscience / Статті в журналах

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Статті в журналах з теми "Robotics and neuroscience"

Автор: Grafiati

Опубліковано: 22 лютого 2025

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1

Laxane, Rahul. "Neuro-Robotics: Bridging Neuroscience and Robotics." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (April 5, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem30166.

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Анотація:

The field of neurorobotics represents the combination of neuroscience and robotics, aiming to elucidate neural functional principles and use them to create intelligent robots. This article considers the symbiotic relationship between the two fields and explores how insights from neuroscience can inform the design and control of robots; Robotic platforms offer a unique opportunity to learn and validate insights from neuroscience. For example, this article focuses on the core concepts of neuroscience and robotics and highlights key advances that support the integration of these fields, including brain-computer interfaces, neurorobotic simulations, and bionic design. It examines how discoveries in neuroscience, such as the understanding of sensorimotor control, learning processes, and cognitive processes, are supporting the creation of biomimetic robots that can address behavioural challenges and interact with their environments.

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2

Floreano, Dario, Auke Jan Ijspeert, and Stefan Schaal. "Robotics and Neuroscience." Current Biology 24, no. 18 (September 2014): R910—R920. http://dx.doi.org/10.1016/j.cub.2014.07.058.

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3

Ferrández, J. M., F. de la Paz, and J. de Lope. "Intelligent robotics and neuroscience." Robotics and Autonomous Systems 58, no. 12 (December 2010): 1221–22. http://dx.doi.org/10.1016/j.robot.2010.09.001.

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4

Pham, Martin Do, Amedeo D’Angiulli, Maryam Mehri Dehnavi, and Robin Chhabra. "From Brain Models to Robotic Embodied Cognition: How Does Biological Plausibility Inform Neuromorphic Systems?" Brain Sciences 13, no. 9 (September 13, 2023): 1316. http://dx.doi.org/10.3390/brainsci13091316.

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We examine the challenging “marriage” between computational efficiency and biological plausibility—A crucial node in the domain of spiking neural networks at the intersection of neuroscience, artificial intelligence, and robotics. Through a transdisciplinary review, we retrace the historical and most recent constraining influences that these parallel fields have exerted on descriptive analysis of the brain, construction of predictive brain models, and ultimately, the embodiment of neural networks in an enacted robotic agent. We study models of Spiking Neural Networks (SNN) as the central means enabling autonomous and intelligent behaviors in biological systems. We then provide a critical comparison of the available hardware and software to emulate SNNs for investigating biological entities and their application on artificial systems. Neuromorphics is identified as a promising tool to embody SNNs in real physical systems and different neuromorphic chips are compared. The concepts required for describing SNNs are dissected and contextualized in the new no man’s land between cognitive neuroscience and artificial intelligence. Although there are recent reviews on the application of neuromorphic computing in various modules of the guidance, navigation, and control of robotic systems, the focus of this paper is more on closing the cognition loop in SNN-embodied robotics. We argue that biologically viable spiking neuronal models used for electroencephalogram signals are excellent candidates for furthering our knowledge of the explainability of SNNs. We complete our survey by reviewing different robotic modules that can benefit from neuromorphic hardware, e.g., perception (with a focus on vision), localization, and cognition. We conclude that the tradeoff between symbolic computational power and biological plausibility of hardware can be best addressed by neuromorphics, whose presence in neurorobotics provides an accountable empirical testbench for investigating synthetic and natural embodied cognition. We argue this is where both theoretical and empirical future work should converge in multidisciplinary efforts involving neuroscience, artificial intelligence, and robotics.

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5

Chawla, Suhani. "ADVANCEMENT OF ROBOTICS IN HEALTHCARE." International Journal of Social Science and Economic Research 07, no. 12 (2022): 3936–52. http://dx.doi.org/10.46609/ijsser.2022.v07i12.006.

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If robots are not common everyday objects, it is maybe because we have looked robotic applications without considering sufficient attention what could be the experience of interacting with a robot. This article introduces the idea of a value profile, a notion intended to capture the general evolution of our experience with different kinds of objects. In the past two decades, robotics has evolved immensely with increased prospects in biological, healthcare, medicine and surgery industry. Robots are being used in almost everything and almost everywhere. However, they are not to replace qualified human workforce, instead, assist them in routine work and precision tasks to achieve high throughput. Advancements in micro- and nano-robotic devices is very much dependent on innovations in micro-electro-mechanical systems (MEMS) and nanoelectromechanical systems (NEMS) with collaborations among diverse domains of research viz., life science, medicine/surgery and engineering. This paper highlights the advancement of Robotics in Neuroscience, Medical Science and IOT in the context of Robotics

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6

Brock, Oliver, and Francisco Valero-Cuevas. "Transferring synergies from neuroscience to robotics." Physics of Life Reviews 17 (July 2016): 27–32. http://dx.doi.org/10.1016/j.plrev.2016.05.011.

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7

Chaminade, Thierry, and Gordon Cheng. "Social cognitive neuroscience and humanoid robotics." Journal of Physiology-Paris 103, no. 3-5 (May 2009): 286–95. http://dx.doi.org/10.1016/j.jphysparis.2009.08.011.

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8

Ronsse, Renaud, Philippe Lefèvre, and Rodolphe Sepulchre. "Robotics and neuroscience: A rhythmic interaction." Neural Networks 21, no. 4 (May 2008): 577–83. http://dx.doi.org/10.1016/j.neunet.2008.03.005.

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9

Schaal, Stefan, Yoshihiko Nakamura, and Paolo Dario. "Special issue on robotics and neuroscience." Neural Networks 21, no. 4 (May 2008): 551–52. http://dx.doi.org/10.1016/j.neunet.2008.04.002.

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10

Da Costa, Lancelot, Pablo Lanillos, Noor Sajid, Karl Friston, and Shujhat Khan. "How Active Inference Could Help Revolutionise Robotics." Entropy 24, no. 3 (March 2, 2022): 361. http://dx.doi.org/10.3390/e24030361.

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Анотація:

Recent advances in neuroscience have characterised brain function using mathematical formalisms and first principles that may be usefully applied elsewhere. In this paper, we explain how active inference—a well-known description of sentient behaviour from neuroscience—can be exploited in robotics. In short, active inference leverages the processes thought to underwrite human behaviour to build effective autonomous systems. These systems show state-of-the-art performance in several robotics settings; we highlight these and explain how this framework may be used to advance robotics.

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11

Cheng, Gordon, Stefan K. Ehrlich, Mikhail Lebedev, and Miguel A. L. Nicolelis. "Neuroengineering challenges of fusing robotics and neuroscience." Science Robotics 5, no. 49 (December 9, 2020): eabd1911. http://dx.doi.org/10.1126/scirobotics.abd1911.

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12

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 (March 2015): 20141250. http://dx.doi.org/10.1098/rsif.2014.1250.

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Анотація:

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 brain and a robot are mutually connected within a closed loop. In this paper, we review the theoretical backgrounds of these three interdisciplinary fields and their recent progress. Then, we introduce recent efforts to reintegrate these research fields into a coherent perspective and propose a new direction that integrates brain science and robotics where the decoding of information from the brain, robot control based on the decoded information and multimodal feedback to the brain from the robot are carried out in real time and in a closed loop.

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13

Dominey, Peter Ford. "Reciprocity between second-person neuroscience and cognitive robotics." Behavioral and Brain Sciences 36, no. 4 (July 25, 2013): 418–19. http://dx.doi.org/10.1017/s0140525x12001884.

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AbstractAs there is “dark matter” in the neuroscience of individuals engaged in dynamic interactions, similar dark matter is present in the domain of interaction between humans and cognitive robots. Progress in second-person neuroscience will contribute to the development of robotic cognitive systems, and such developed robotic systems will be used to test the validity of the underlying theories.

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14

Oña, E. D., R. Cano-de la Cuerda, P. Sánchez-Herrera, C. Balaguer, and A. Jardón. "A Review of Robotics in Neurorehabilitation: Towards an Automated Process for Upper Limb." Journal of Healthcare Engineering 2018 (2018): 1–19. http://dx.doi.org/10.1155/2018/9758939.

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Robot-mediated neurorehabilitation is a growing field that seeks to incorporate advances in robotics combined with neuroscience and rehabilitation to define new methods for treating problems related with neurological diseases. In this paper, a systematic literature review is conducted to identify the contribution of robotics for upper limb neurorehabilitation, highlighting its relation with the rehabilitation cycle, and to clarify the prospective research directions in the development of more autonomous rehabilitation processes. With this aim, first, a study and definition of a general rehabilitation process are made, and then, it is particularized for the case of neurorehabilitation, identifying the components involved in the cycle and their degree of interaction between them. Next, this generic process is compared with the current literature in robotics focused on upper limb treatment, analyzing which components of this rehabilitation cycle are being investigated. Finally, the challenges and opportunities to obtain more autonomous rehabilitation processes are discussed. In addition, based on this study, a series of technical requirements that should be taken into account when designing and implementing autonomous robotic systems for rehabilitation is presented and discussed.

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15

Kawato, Mitsuo, and Kazuyuki Samejima. "Efficient reinforcement learning: computational theories, neuroscience and robotics." Current Opinion in Neurobiology 17, no. 2 (April 2007): 205–12. http://dx.doi.org/10.1016/j.conb.2007.03.004.

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16

Jamone, Lorenzo, Emre Ugur, Angelo Cangelosi, Luciano Fadiga, Alexandre Bernardino, Justus Piater, and Jose Santos-Victor. "Affordances in Psychology, Neuroscience, and Robotics: A Survey." IEEE Transactions on Cognitive and Developmental Systems 10, no. 1 (March 2018): 4–25. http://dx.doi.org/10.1109/tcds.2016.2594134.

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17

Nassour, John, Tran Duy Hoa, Payam Atoofi, and Fred Hamker. "Concrete Action Representation Model: From Neuroscience to Robotics." IEEE Transactions on Cognitive and Developmental Systems 12, no. 2 (June 2020): 272–84. http://dx.doi.org/10.1109/tcds.2019.2896300.

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18

Ekambaram, Rajasekaran, Meenal Rajasekaran, and Devprakash Rajasekaran. "Preservation of Human Essence: A Technological Evolution of Identity." International Journal of Multidisciplinary Research and Growth Evaluation 6, no. 1 (2025): 1138–44. https://doi.org/10.54660/.ijmrge.2025.6.1.1138-1144.

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Анотація:

The preservation of human essence in robotics represents a groundbreaking confluence of neuroscience, artificial intelligence and advanced sensory technologies. This article explores how cognitive continuity, emotional simulation, sensory perception and memory preservation contribute to maintaining individuality and human identity within robotic entities. Neural mapping digitizes thought patterns, algorithms replicate emotional responses and advanced sensors emulate human senses. Ethical considerations surrounding consent and equitable access are crucial to this transformative journey. By enabling humanity to transcend biological limitations, this approach redefines identity and legacy in a technologically evolved world.

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19

Cross, Emily S., Ruud Hortensius, and Agnieszka Wykowska. "From social brains to social robots: applying neurocognitive insights to human–robot interaction." Philosophical Transactions of the Royal Society B: Biological Sciences 374, no. 1771 (March 11, 2019): 20180024. http://dx.doi.org/10.1098/rstb.2018.0024.

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Amidst the fourth industrial revolution, social robots are resolutely moving from fiction to reality. With sophisticated artificial agents becoming ever more ubiquitous in daily life, researchers across different fields are grappling with the questions concerning how humans perceive and interact with these agents and the extent to which the human brain incorporates intelligent machines into our social milieu. This theme issue surveys and discusses the latest findings, current challenges and future directions in neuroscience- and psychology-inspired human–robot interaction (HRI). Critical questions are explored from a transdisciplinary perspective centred around four core topics in HRI: technical solutions for HRI, development and learning for HRI, robots as a tool to study social cognition, and moral and ethical implications of HRI. Integrating findings from diverse but complementary research fields, including social and cognitive neurosciences, psychology, artificial intelligence and robotics, the contributions showcase ways in which research from disciplines spanning biological sciences, social sciences and technology deepen our understanding of the potential and limits of robotic agents in human social life. This article is part of the theme issue ‘From social brains to social robots: applying neurocognitive insights to human–robot interaction’.

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20

Azevedo, Christine, Bernard Espiau, Bernard Amblard, and Christine Assaiante. "Bipedal locomotion: toward unified concepts in robotics and neuroscience." Biological Cybernetics 96, no. 2 (November 21, 2006): 209–28. http://dx.doi.org/10.1007/s00422-006-0118-0.

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21

Liu, Sichao, Lihui Wang, and Robert X. Gao. "Cognitive neuroscience and robotics: Advancements and future research directions." Robotics and Computer-Integrated Manufacturing 85 (February 2024): 102610. http://dx.doi.org/10.1016/j.rcim.2023.102610.

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22

Jones, Alexander, Vaibhav Gandhi, Adam Y. Mahiddine, and Christian Huyck. "Bridging Neuroscience and Robotics: Spiking Neural Networks in Action." Sensors 23, no. 21 (November 1, 2023): 8880. http://dx.doi.org/10.3390/s23218880.

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Анотація:

Robots are becoming increasingly sophisticated in the execution of complex tasks. However, an area that requires development is the ability to act in dynamically changing environments. To advance this, developments have turned towards understanding the human brain and applying this to improve robotics. The present study used electroencephalogram (EEG) data recorded from 54 human participants whilst they performed a two-choice task. A build-up of motor activity starting around 400 ms before response onset, also known as the lateralized readiness potential (LRP), was observed. This indicates that actions are not simply binary processes but rather, response-preparation is gradual and occurs in a temporal window that can interact with the environment. In parallel, a robot arm executing a pick-and-place task was developed. The understanding from the EEG data and the robot arm were integrated into the final system, which included cell assemblies (CAs)—a simulated spiking neural network—to inform the robot to place the object left or right. Results showed that the neural data from the robot simulation were largely consistent with the human data. This neurorobotics study provides an example of how to integrate human brain recordings with simulated neural networks in order to drive a robot.

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23

Ijspeert, Auke J. "Amphibious and Sprawling Locomotion: From Biology to Robotics and Back." Annual Review of Control, Robotics, and Autonomous Systems 3, no. 1 (May 3, 2020): 173–93. http://dx.doi.org/10.1146/annurev-control-091919-095731.

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Анотація:

A milestone in vertebrate evolution, the transition from water to land, owes its success to the development of a sprawling body plan that enabled an amphibious lifestyle. The body, originally adapted for swimming, evolved to benefit from limbs that enhanced its locomotion capabilities on submerged and dry ground. The first terrestrial animals used sprawling locomotion, a type of legged locomotion in which limbs extend laterally from the body (as opposed to erect locomotion, in which limbs extend vertically below the body). This type of locomotion—exhibited, for instance, by salamanders, lizards, and crocodiles—has been studied in a variety of fields, including neuroscience, biomechanics, evolution, and paleontology. Robotics can benefit from these studies to design amphibious robots capable of swimming and walking, with interesting applications in field robotics, in particular for search and rescue, inspection, and environmental monitoring. In return, robotics can provide useful scientific tools to test hypotheses in neuroscience, biomechanics, and paleontology. For instance, robots have been used to test hypotheses about the organization of neural circuits that can switch between swimming and walking under the control of simple modulation signals, as well as to identify the most likely gaits of extinct sprawling animals. Here, I review different aspects of amphibious and sprawling locomotion, namely gait characteristics, neurobiology, numerical models, and sprawling robots, and discuss fruitful interactions between robotics and other scientific fields.

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24

Pepperberg, Irene M. "The conundrum of correlation and causation." Behavioral and Brain Sciences 24, no. 6 (December 2001): 1073–74. http://dx.doi.org/10.1017/s0140525x01460122.

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Biology can inspire robotic simulations of behavior and thus advance robotics, but the validity of drawing conclusions about real behavior from robotic models is questionable. Robotic models, particularly of learning, do not account, for example, for (a) exaptation: co-opting of previously evolved functions for new behavior, (b) learning through observation, (c) complex biological reality, or (d) limits on computational capacity.

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25

Hellström, Thomas. "The relevance of causation in robotics: A review, categorization, and analysis." Paladyn, Journal of Behavioral Robotics 12, no. 1 (January 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. Overall, we conclude that causation underlies several problem formulations in robotics, but it is still surprisingly absent in published research, in particular when it comes to explicit mentioning and using of causal concepts and terms. We discuss the reasons for, and consequences of, this and hope that this article clarifies the broad and deep connections between causal reasoning and robotics and also by pointing at the close connections to other research areas. At best, this will also contribute to a “causal revolution” in robotics.

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26

Berthouze, Luc, and Giorgio Metta. "Epigenetic robotics: modelling cognitive development in robotic systems." Cognitive Systems Research 6, no. 3 (September 2005): 189–92. http://dx.doi.org/10.1016/j.cogsys.2004.11.002.

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27

Takahashi, Hideyuki, and Hisashi Ishihara. "Social-neuro robotics as a Method for Social Cognitive Neuroscience." Journal of the Robotics Society of Japan 31, no. 9 (2013): 840–43. http://dx.doi.org/10.7210/jrsj.31.840.

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28

Rucci, Michele, Daniel Bullock, and Fabrizio Santini. "Integrating robotics and neuroscience: brains for robots, bodies for brains." Advanced Robotics 21, no. 10 (January 2007): 1115–29. http://dx.doi.org/10.1163/156855307781389428.

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29

Pizzino, Carlos Alexandre Pontes, Ramon Romankevicius Costa, Daniel Mitchell, and Patrícia Amâncio Vargas. "NeoSLAM: Long-Term SLAM Using Computational Models of the Brain." Sensors 24, no. 4 (February 9, 2024): 1143. http://dx.doi.org/10.3390/s24041143.

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Анотація:

Simultaneous Localization and Mapping (SLAM) is a fundamental problem in the field of robotics, enabling autonomous robots to navigate and create maps of unknown environments. Nevertheless, the SLAM methods that use cameras face problems in maintaining accurate localization over extended periods across various challenging conditions and scenarios. Following advances in neuroscience, we propose NeoSLAM, a novel long-term visual SLAM, which uses computational models of the brain to deal with this problem. Inspired by the human neocortex, NeoSLAM is based on a hierarchical temporal memory model that has the potential to identify temporal sequences of spatial patterns using sparse distributed representations. Being known to have a high representational capacity and high tolerance to noise, sparse distributed representations have several properties, enabling the development of a novel neuroscience-based loop-closure detector that allows for real-time performance, especially in resource-constrained robotic systems. The proposed method has been thoroughly evaluated in terms of environmental complexity by using a wheeled robot deployed in the field and demonstrated that the accuracy of loop-closure detection was improved compared with the traditional RatSLAM system.

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30

Duchon, Andrew P., Leslie Pack Kaelbling, and William H. Warren. "Ecological Robotics." Adaptive Behavior 6, no. 3-4 (January 1998): 473–507. http://dx.doi.org/10.1177/105971239800600306.

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31

Dodig-Crnkovic, G. "Natural morphological computation as foundation of learning to learn in humans, other living organisms, and intelligent machines." Philosophical Problems of Information Technologies and Cyberspace, no. 1 (July 14, 2021): 4–34. http://dx.doi.org/10.17726/philit.2021.1.1.

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Анотація:

The emerging contemporary natural philosophy provides a common ground for the integrative view of the natural, the artificial, and the human-social knowledge and practices. Learning process is central for acquiring, maintaining, and managing knowledge, both theoretical and practical. This paper explores the relationships between the present advances in understanding of learning in the sciences of the artificial (deep learning, robotics), natural sciences (neuroscience, cognitive science, biology), and philosophy (philosophy of computing, philosophy of mind, natural philosophy). The question is, what at this stage of the development the inspiration from nature, specifically its computational models such as info-computation through morphological computing, can contribute to machine learning and artificial intelligence, and how much on the other hand models and experiments in machine learning and robotics can motivate, justify, and inform research in computational cognitive science, neurosciences, and computing nature. We propose that one contribution can be understanding of the mechanisms of ‘learning to learn’, as a step towards deep learning with symbolic layer of computation/information processing in a framework linking connectionism with symbolism. As all natural systems possessing intelligence are cognitive systems, we describe the evolutionary arguments for the necessity of learning to learn for a system to reach humanlevel intelligence through evolution and development. The paper thus presents a contribution to the epistemology of the contemporary philosophy of nature.

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32

Dodig-Crnkovic, Gordana. "Natural Morphological Computation as Foundation of Learning to Learn in Humans, Other Living Organisms, and Intelligent Machines." Philosophies 5, no. 3 (September 1, 2020): 17. http://dx.doi.org/10.3390/philosophies5030017.

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Анотація:

The emerging contemporary natural philosophy provides a common ground for the integrative view of the natural, the artificial, and the human-social knowledge and practices. Learning process is central for acquiring, maintaining, and managing knowledge, both theoretical and practical. This paper explores the relationships between the present advances in understanding of learning in the sciences of the artificial (deep learning, robotics), natural sciences (neuroscience, cognitive science, biology), and philosophy (philosophy of computing, philosophy of mind, natural philosophy). The question is, what at this stage of the development the inspiration from nature, specifically its computational models such as info-computation through morphological computing, can contribute to machine learning and artificial intelligence, and how much on the other hand models and experiments in machine learning and robotics can motivate, justify, and inform research in computational cognitive science, neurosciences, and computing nature. We propose that one contribution can be understanding of the mechanisms of ‘learning to learn’, as a step towards deep learning with symbolic layer of computation/information processing in a framework linking connectionism with symbolism. As all natural systems possessing intelligence are cognitive systems, we describe the evolutionary arguments for the necessity of learning to learn for a system to reach human-level intelligence through evolution and development. The paper thus presents a contribution to the epistemology of the contemporary philosophy of nature.

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33

Blanke, Olaf. "Brain stimulation and neuroscience robotics for induction and assessment of hallucinations." Brain Stimulation 16, no. 1 (January 2023): 119. http://dx.doi.org/10.1016/j.brs.2023.01.019.

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34

Liu, Rongrong, Florent Nageotte, Philippe Zanne, Michel de Mathelin, and Birgitta Dresp-Langley. "Wearable Wireless Biosensors for Spatiotemporal Grip Force Profiling in Real Time." Engineering Proceedings 2, no. 1 (November 14, 2020): 45. http://dx.doi.org/10.3390/ecsa-7-08252.

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Анотація:

The temporal evolution of individual grip force profiles of a novice using a robotic system for minimally invasive endoscopic surgery is analyzed on the basis of thousands of individual sensor data recorded in real time through a wearable wireless sensor glove system. The spatio-temporal grip force profiles from specific sensor locations in the dominant hand performing a four-step pick-and-drop simulator task reveal skill-relevant differences in force deployment by the small finger (fine grip force control) and the middle finger (gross grip force contribution) by comparison with the profiles of a highly proficient expert. Cross-disciplinary insights from systems neuroscience, cognitive behavioral science, and robotics, with implications for biologically inspired AI for human–robot interactions, highlight the functional significance of spatio-temporal grip force profiling.

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35

Herrera Pérez, Carlos, María Guadalupe Sánchez-Escribano, and Ricardo Sanz. "The morphofunctional approach to emotion modelling in robotics." Adaptive Behavior 20, no. 5 (July 16, 2012): 388–404. http://dx.doi.org/10.1177/1059712312451604.

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In this conceptual paper, we discuss two areas of research in robotics, robotic models of emotion and morphofunctional machines, and we explore the scope for potential cross-fertilization between them. We shift the focus in robot models of emotion from information-theoretic aspects of appraisal to the interactive significance of bodily dispositions. Typical emotional phenomena such as arousal and action readiness can be interpreted as morphofunctional processes, and their functionality may be replicated in robotic systems with morphologies that can be modulated for real-time adaptation. We investigate the control requirements for such systems, and present a possible bio-inspired architecture, based on the division of control between neural and endocrine systems in humans and animals. We suggest that emotional episodes can be understood as emergent from the coordination of action control and action-readiness, respectively. This stress on morphology complements existing research on the information-theoretic aspects of emotion.

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De Jaegher, Hanne, Ezequiel Di Paolo, and Ralph Adolphs. "What does the interactive brain hypothesis mean for social neuroscience? A dialogue." Philosophical Transactions of the Royal Society B: Biological Sciences 371, no. 1693 (May 5, 2016): 20150379. http://dx.doi.org/10.1098/rstb.2015.0379.

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A recent framework inspired by phenomenological philosophy, dynamical systems theory, embodied cognition and robotics has proposed the interactive brain hypothesis (IBH). Whereas mainstream social neuroscience views social cognition as arising solely from events in the brain, the IBH argues that social cognition requires, in addition, causal relations between the brain and the social environment. We discuss, in turn, the foundational claims for the IBH in its strongest form; classical views of cognition that can be raised against the IBH; a defence of the IBH in the light of these arguments; and a response to this. Our goal is to initiate a dialogue between cognitive neuroscience and enactive views of social cognition. We conclude by suggesting some new directions and emphases that social neuroscience might take.

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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 (October 20, 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 principles of evo-devo-robo that have been developed so far are discussed. Findings – Finally, some misunderstand concepts and the most promising future research developments in this area are discussed. Originality/value – Basic concepts and the origins of the field are described, and then some basic principles of evo-devo-robo that have been developed so far are discussed. Finally, some misunderstood concepts and the most promising future research developments in this area are discussed.

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Gupta, Nalina, and Kavitha Raja. "Rehabilitation robotics in India." Journal of Neurosciences in Rural Practice 02, no. 02 (July 2011): 207–9. http://dx.doi.org/10.4103/0976-3147.83604.

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van der Smagt, Patrick, Markus Grebenstein, Holger Urbanek, Nadine Fligge, Michael Strohmayr, Georg Stillfried, Jonathon Parrish, and Agneta Gustus. "Robotics of human movements." Journal of Physiology-Paris 103, no. 3-5 (May 2009): 119–32. http://dx.doi.org/10.1016/j.jphysparis.2009.07.009.

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Sakamoto, Kazuhiro, Hiroaki Wagatsuma, and Kaori Tachibana. "Seven Years of the Workshop for Synergetics between Neuroscience, Rehabilitation and Robotics." Brain & Neural Networks 23, no. 4 (2016): 169–75. http://dx.doi.org/10.3902/jnns.23.169.

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d'Avella, Andrea. "Integration of robotics and neuroscience beyond the hand: What kind of synergies?" Physics of Life Reviews 17 (July 2016): 33–35. http://dx.doi.org/10.1016/j.plrev.2016.04.001.

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Pransky, Joanne. "The Pransky interview: Dr Maja Matarić, Professor, University of Southern California; Pioneer, field of socially assistive robotics; co-founder of Embodied." Industrial Robot: the international journal of robotics research and application 46, no. 3 (May 20, 2019): 332–36. http://dx.doi.org/10.1108/ir-04-2019-0069.

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Purpose The following paper is a “Q&A interview” conducted by Joanne Pransky of Industrial Robot Journal as a method to impart the combined technological, business and personal experience of a prominent, robotic industry PhD and innovator regarding her pioneering efforts and the challenges of bringing a technological invention to market. This paper aims to discuss these issues. Design/methodology/approach The interviewee is Dr Maja Matarić, Chan Soon-Shiong Distinguished Professor in the Computer Science Department, Neuroscience Program, and the Department of Pediatrics at the University of Southern California, founding director of the USC Robotics and Autonomous Systems Center (RASC), co-director of the USC Robotics Research Lab and Vice Dean for Research in the USC Viterbi School of Engineering. In this interview, Matarić shares her personal and business perspectives on socially assistive robotics. Findings Matarić received her PhD in Computer Science and Artificial Intelligence from MIT in 1994, MS in Computer Science from MIT in 1990 and BS in Computer Science from the University of Kansas in 1987. Inspired by the vast potential for affordable human-centered technologies, she went on to found and direct the Interaction Lab, initially at Brandeis University and then at the University of Southern California. Her lab works on developing human–robot non-physical interaction algorithms for supporting desirable behavior change; she has worked with a variety of beneficiary user populations, including children with autism, elderly with Alzheimer’s, stroke survivors and teens at risk for Type 2 diabetes, among others. Originality/value Matarić is a pioneer of the field of socially assistive robotics (SAR) with the goal of improving user health and wellness, communication, learning and autonomy. SAR uses interdisciplinary methods from computer science and engineering as well as cognitive science, social science and human studies evaluation, to endow robots with the ability to assist in mitigating critical societal problems that require sustained personalized support to supplement the efforts of parents, caregivers, clinicians and educators. Matarić is a Fellow of the American Association for the Advancement of Science (AAAS), Fellow of the IEEE and AAAI, recipient of the Presidential Awards for Excellence in Science, Mathematics & Engineering Mentoring (PAESMEM), the Anita Borg Institute Women of Vision Award for Innovation, Okawa Foundation Award, NSF Career Award, the MIT TR35 Innovation Award, the IEEE Robotics and Automation Society Early Career Award and has received many other awards and honors. She was featured in the science documentary movie “Me & Isaac Newton”, in The New Yorker (“Robots that Care” by Jerome Groopman, 2009), Popular Science (“The New Face of Autism Therapy”, 2010), the IEEE Spectrum (“Caregiver Robots”, 2010), and is one of the LA Times Magazine 2010 Visionaries. Matarić is the author of a popular introductory robotics textbook, “The Robotics Primer” (MIT Press 2007), an associate editor of three major journals and has published extensively.

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Anil Meera, Ajith, and Martijn Wisse. "Dynamic Expectation Maximization Algorithm for Estimation of Linear Systems with Colored Noise." Entropy 23, no. 10 (October 5, 2021): 1306. http://dx.doi.org/10.3390/e23101306.

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The free energy principle from neuroscience has recently gained traction as one of the most prominent brain theories that can emulate the brain’s perception and action in a bio-inspired manner. This renders the theory with the potential to hold the key for general artificial intelligence. Leveraging this potential, this paper aims to bridge the gap between neuroscience and robotics by reformulating an FEP-based inference scheme—Dynamic Expectation Maximization—into an algorithm that can perform simultaneous state, input, parameter, and noise hyperparameter estimation of any stable linear state space system subjected to colored noises. The resulting estimator was proved to be of the form of an augmented coupled linear estimator. Using this mathematical formulation, we proved that the estimation steps have theoretical guarantees of convergence. The algorithm was rigorously tested in simulation on a wide variety of linear systems with colored noises. The paper concludes by demonstrating the superior performance of DEM for parameter estimation under colored noise in simulation, when compared to the state-of-the-art estimators like Sub Space method, Prediction Error Minimization (PEM), and Expectation Maximization (EM) algorithm. These results contribute to the applicability of DEM as a robust learning algorithm for safe robotic applications.

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Wu, Jiajun. "Learning to See the Physical World." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (June 26, 2023): 15460. http://dx.doi.org/10.1609/aaai.v37i13.26827.

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This paper is part of the AAAI-23 New Faculty Highlights. In my presentation, I will introduce my research goal, which is to build machines that see, interact with, and reason about the physical world just like humans. This problem, which we call physical scene understanding, involves three key topics that bridge research in computer science, AI, robotics, cognitive science, and neuroscience: Perception, Physical Interaction, and Reasoning.

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Cliff, Dave, Phil Husbands, and Inman Harvey. "Explorations in Evolutionary Robotics." Adaptive Behavior 2, no. 1 (June 1993): 73–110. http://dx.doi.org/10.1177/105971239300200104.

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Clark, Andy, and Rick Grush. "Towards a Cognitive Robotics." Adaptive Behavior 7, no. 1 (January 1999): 5–16. http://dx.doi.org/10.1177/105971239900700101.

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Rogatkin, Dmitry A., Dmitry A. Kulikov, and Aleksandra L. Ivlieva. "Three Views on Current Data of Neuroscience for the Purposes of Intelligent Robotics." Modeling of Artificial Intelligence 6, no. 2 (June 15, 2015): 98–136. http://dx.doi.org/10.13187/mai.2015.6.98.

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Mohan, Vishwanathan, Ajaz Bhat, and Pietro Morasso. "Muscleless motor synergies and actions without movements: From motor neuroscience to cognitive robotics." Physics of Life Reviews 30 (October 2019): 89–111. http://dx.doi.org/10.1016/j.plrev.2018.04.005.

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Kodandaramaiah, Suhasa B., Edward S. Boyden, and Craig R. Forest. "In vivo robotics: the automation of neuroscience and other intact‐system biological fields." Annals of the New York Academy of Sciences 1305, no. 1 (July 10, 2013): 63–71. http://dx.doi.org/10.1111/nyas.12171.

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Hooijmans, Marti, and Fred Keijzer. "Robotics, biological grounding and the Fregean tradition." Mechanicism and Autonomy: What Can Robotics Teach Us About Human Cognition and Action? 15, no. 3 (December 13, 2007): 515–46. http://dx.doi.org/10.1075/pc.15.3.08hoo.

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Dynamic, embodied and situated cognition set up organism-environment interaction — agency for short — as the core of cognitive systems. Robotics became an important way to study this behavioral kernel of cognition. In this paper, we discuss the implications of what we call the biological grounding problem for robotic studies: Natural and artificial agents are hugely different and it will be necessary to articulate what must be replicated by artificial agents such as robots. Interestingly, once this issue is explicitly raised, it seems that a full replication of biological features is required for cognition itself to be plausibly cast as a biological phenomenon. Several issues come to the fore once one takes this implication seriously. Why does a full biological interpretation of cognition remain so controversial? How does this impact the relevance of robotics for the study of cognition? We try to articulate and ease the various tensions that arise from this biological scenario.

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