Reciprocity between second-person neuroscience and cognitive robotics

2013 ◽  
Vol 36 (4) ◽  
pp. 418-419
Author(s):  
Peter Ford Dominey
Keyword(s):  
Dark Matter ◽  
Cognitive Robotics ◽  
Robotic Systems ◽  
Cognitive Systems ◽  
Second Person ◽  
Dynamic Interactions ◽  
Cognitive Robots

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.

scholarly journals Enaction as a Conceptual Framework for Developmental Cognitive Robotics

2010 ◽  
Vol 1 (2) ◽  
Author(s):  
David Vernon
Keyword(s):  
Cognitive Abilities ◽  
Humanoid Robot ◽  
Cognitive Architecture ◽  
Cognitive System ◽  
Cognitive Robotics ◽  
Robotic Systems ◽  
Sense Making ◽  
Cognitive Systems ◽  
Core Set ◽  
The World

AbstractThis paper provides an accessible introduction to the cognitive systems paradigm of enaction and shows how it forms a practical framework for robotic systems that can develop cognitive abilities. The principal idea of enaction is that a cognitive system develops it own understanding of the world around it through its interactions with the environment. Thus, enaction entails that the cognitive system operates autonomously and that it generates its own models of how the world works. A discussion of the five key elements of enaction — autonomy, embodiment, emergence, experience, and sense-making — leads to a core set of functional, organizational, and developmental requirements which are then used in the design of a cognitive architecture for the iCub humanoid robot.

scholarly journals Abstract Intelligence

2017 ◽  
Vol 11 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Yingxu Wang ◽  
Lotfi A. Zadeh ◽  
Bernard Widrow ◽  
Newton Howard ◽  
Françoise Beaufays ◽  
...  
Keyword(s):  
Neural Networks ◽  
Mathematical Models ◽  
Computational Intelligence ◽  
Cognitive Learning ◽  
Cognitive Systems ◽  
Stanford University ◽  
Cognitive Computing ◽  
Cognitive Informatics ◽  
Basic Studies ◽  
Cognitive Robots

Basic studies in denotational mathematics and mathematical engineering have led to the theory of abstract intelligence (aI), which is a set of mathematical models of natural and computational intelligence in cognitive informatics (CI) and cognitive computing (CC). Abstract intelligence triggers the recent breakthroughs in cognitive systems such as cognitive computers, cognitive robots, cognitive neural networks, and cognitive learning. This paper reports a set of position statements presented in the plenary panel (Part II) of IEEE ICCI*CC'16 on Cognitive Informatics and Cognitive Computing at Stanford University. The summary is contributed by invited panelists who are part of the world's renowned scholars in the transdisciplinary field of CI and CC.

scholarly journals Abstract Concept Learning in Cognitive Robots

2021 ◽  
Author(s):  
Alessandro Di Nuovo ◽  
Angelo Cangelosi
Keyword(s):  
Concept Learning ◽  
Cognitive Robotics ◽  
Abstract Concept ◽  
Abstract Concepts ◽  
Knowledge Based ◽  
Abstract Knowledge ◽  
Benchmark Datasets ◽  
Artificial Learning ◽  
Concrete Concepts ◽  
Cognitive Robots

Abstract Purpose of Review Understanding and manipulating abstract concepts is a fundamental characteristic of human intelligence that is currently missing in artificial agents. Without it, the ability of these robots to interact socially with humans while performing their tasks would be hindered. However, what is needed to empower our robots with such a capability? In this article, we discuss some recent attempts on cognitive robot modeling of these concepts underpinned by some neurophysiological principles. Recent Findings For advanced learning of abstract concepts, an artificial agent needs a (robotic) body, because abstract and concrete concepts are considered a continuum, and abstract concepts can be learned by linking them to concrete embodied perceptions. Pioneering studies provided valuable information about the simulation of artificial learning and demonstrated the value of the cognitive robotics approach to study aspects of abstract cognition. Summary There are a few successful examples of cognitive models of abstract knowledge based on connectionist and probabilistic modeling techniques. However, the modeling of abstract concept learning in robots is currently limited at narrow tasks. To make further progress, we argue that closer collaboration among multiple disciplines is required to share expertise and co-design future studies. Particularly important is to create and share benchmark datasets of human learning behavior.

scholarly journals Deep learning for semantic segmentation of organs and tissues in laparoscopic surgery

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Paul Maria Scheikl ◽  
Stefan Laschewski ◽  
Anna Kisilenko ◽  
Tornike Davitashvili ◽  
Benjamin Müller ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Laparoscopic Surgery ◽  
Semantic Segmentation ◽  
Cognitive Robotics ◽  
Loss Functions ◽  
Cognitive Systems ◽  
Context Aware ◽  
Segmentation Quality ◽  
And Training

AbstractSemantic segmentation of organs and tissue types is an important sub-problem in image based scene understanding for laparoscopic surgery and is a prerequisite for context-aware assistance and cognitive robotics. Deep Learning (DL) approaches are prominently applied to segmentation and tracking of laparoscopic instruments. This work compares different combinations of neural networks, loss functions, and training strategies in their application to semantic segmentation of different organs and tissue types in human laparoscopic images in order to investigate their applicability as components in cognitive systems. TernausNet-11 trained on Soft-Jaccard loss with a pretrained, trainable encoder performs best in regard to segmentation quality (78.31% mean Intersection over Union [IoU]) and inference time (28.07 ms) on a single GTX 1070 GPU.

Cognitive Robotics on 5G Networks

2021 ◽  
Vol 21 (4) ◽  
pp. 1-18
Author(s):  
Zhihan Lv ◽  
Liang Qiao ◽  
Qingjun Wang
Keyword(s):  
Information Processing ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Principal Component ◽  
Cognitive Robotics ◽  
Human Information Processing ◽  
Expression Recognition ◽  
Multi Scale ◽  
Technical Indicator ◽  
Cognitive Robots

Emotional cognitive ability is a key technical indicator to measure the friendliness of interaction. Therefore, this research aims to explore robots with human emotion cognitively. By discussing the prospects of 5G technology and cognitive robots, the main direction of the study is cognitive robots. For the emotional cognitive robots, the analysis logic similar to humans is difficult to imitate; the information processing levels of robots are divided into three levels in this study: cognitive algorithm, feature extraction, and information collection by comparing human information processing levels. In addition, a multi-scale rectangular direction gradient histogram is used for facial expression recognition, and robust principal component analysis algorithm is used for facial expression recognition. In the pictures where humans intuitively feel smiles in sad emotions, the proportion of emotions obtained by the method in this study are as follows: calmness accounted for 0%, sadness accounted for 15.78%, fear accounted for 0%, happiness accounted for 76.53%, disgust accounted for 7.69%, anger accounted for 0%, and astonishment accounted for 0%. In the recognition of micro-expressions, humans intuitively feel negative emotions such as surprise and fear, and the proportion of emotions obtained by the method adopted in this study are as follows: calmness accounted for 32.34%, sadness accounted for 34.07%, fear accounted for 6.79%, happiness accounted for 0%, disgust accounted for 0%, anger accounted for 13.91%, and astonishment accounted for 15.89%. Therefore, the algorithm explored in this study can realize accuracy in cognition of emotions. From the preceding research results, it can be seen that the research method in this study can intuitively reflect the proportion of human expressions, and the recognition methods based on facial expressions and micro-expressions have good recognition effects, which is in line with human intuitive experience.

scholarly journals Cognitive Informatics

2018 ◽  
Vol 12 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Yingxu Wang ◽  
Newton Howard ◽  
Janusz Kacprzyk ◽  
Ophir Frieder ◽  
Phillip Sheu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computational Intelligence ◽  
Cognitive Robotics ◽  
Cognitive Systems ◽  
Cognitive Computing ◽  
Cognitive Informatics ◽  
Oxford University ◽  
Basic Studies ◽  
Intelligence Theories ◽  
The Brain

Cognitive Informatics (CI) is a contemporary field of basic studies on the brain, computational intelligence theories and underpinning denotational mathematics. Its applications include cognitive systems, cognitive computing, cognitive machine learning and cognitive robotics. IEEE ICCI*CC'17 on Cognitive Informatics and Cognitive Computing was focused on the theme of neurocomputation, cognitive machine learning and brain-inspired systems. This paper reports the plenary panel (Part I) at IEEE ICCI*CC'17 held at Oxford University. The summary is contributed by invited keynote speakers and distinguished panelists who are part of the world's renowned scholars in the transdisciplinary field of CI and cognitive computing.

scholarly journals On the philosophical, cognitive and mathematical foundations of symbiotic autonomous systems

2021 ◽  
Vol 379 (2207) ◽  
pp. 20200362
Author(s):  
Yingxu Wang ◽  
Fakhri Karray ◽  
Sam Kwong ◽  
Konstantinos N. Plataniotis ◽  
Henry Leung ◽  
...  
Keyword(s):  
Intelligent Systems ◽  
Collective Intelligence ◽  
Autonomous Systems ◽  
Basic Research ◽  
Machine Intelligence ◽  
Cognitive Systems ◽  
Intelligent Machines ◽  
Autonomous Machine ◽  
Cognitive Robots ◽  
Mathematical Foundations

Symbiotic autonomous systems (SAS) are advanced intelligent and cognitive systems that exhibit autonomous collective intelligence enabled by coherent symbiosis of human–machine interactions in hybrid societies. Basic research in the emerging field of SAS has triggered advanced general-AI technologies that either function without human intervention or synergize humans and intelligent machines in coherent cognitive systems. This work presents a theoretical framework of SAS underpinned by the latest advances in intelligence, cognition, computer, and system sciences. SAS are characterized by the composition of autonomous and symbiotic systems that adopt bio-brain-social-inspired and heterogeneously synergized structures and autonomous behaviours. This paper explores the cognitive and mathematical foundations of SAS. The challenges to seamless human–machine interactions in a hybrid environment are addressed. SAS-based collective intelligence is explored in order to augment human capability by autonomous machine intelligence towards the next generation of general AI, cognitive computers, and trustworthy mission-critical intelligent systems. Emerging paradigms and engineering applications of SAS are elaborated via autonomous knowledge learning systems that symbiotically work between humans and cognitive robots. This article is part of the theme issue ‘Towards symbiotic autonomous systems'.

Designing Useful Robots

2011 ◽  
pp. 250-269
Author(s):  
David Bisset
Keyword(s):  
Product Design ◽  
Adaptive Systems ◽  
Neural Computation ◽  
Robotic Systems ◽  
Cognitive Systems ◽  
Design Issues ◽  
Ethical Problems ◽  
Current State ◽  
And Function ◽  
Business Sectors

This chapter explores the challenges presented by the introduction of robots into our everyday lives, examining technical and design issues as well as ethical and business issues. It also examines the process of designing and specifying useful robots and highlights the practical difficulties in testing and guaranteeing behaviour and function in adaptive systems. The chapter also briefly reviews the current state of robotics in Europe and the global robotic marketplace. It argues that it is essential, for the generation of a viable industry, for the Academic and Business sectors to work together to solve the fundamental technical and ethical problems that can potentially impede the development and deployment of autonomous robotic systems. It details the reality and expectations in healthcare robotics examining the demographics and deployment difficulties this domain will face. Finally it challenges the assumption that Neural Computation is the technology of choice for building autonomous cognitive systems and points out the difficulties inherent in using adaptive “holistic” systems within the performance oriented ethos of the product design engineer.

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