The Experts below are selected from a list of 5103 Experts worldwide ranked by ideXlab platform

Angelo Cangelosi - One of the best experts on this subject based on the ideXlab platform.

  • Aquila: An Open-Source GPU-Accelerated Toolkit for Cognitive Robotics Research
    2020
    Co-Authors: Martin Peniak, Anthony F. Morse, Christopher Larcombe, Salomon Ramirez-contla, Angelo Cangelosi
    Abstract:

    This paper presents a novel open-source software Aquila developed as a part of the iTalk and RobotDoC projects. This software provides many different tools and biologically inspired systems that are useful for Cognitive Robotics research. Aquila addresses the need for high-performance robot control by adopting the latest parallel processing paradigm based on the NVidia CUDA technology. The software philosophy, implementation, functionalities and performance are described together with three practical examples of selected modules.

  • ICDL-EPIROB - Aquila 2.0 software architecture for Cognitive Robotics
    2013 IEEE Third Joint International Conference on Development and Learning and Epigenetic Robotics (ICDL), 2013
    Co-Authors: Martin Peniak, Anthony F. Morse, Angelo Cangelosi
    Abstract:

    The modelling of the integration of various Cognitive skills and modalities requires complex and computationally intensive algorithms running in parallel while controlling high-performance systems. The distribution of processing across many computers has certainly advanced our software ecosystem and opened up research to new possibilities. While this was an essential move, we are aspiring to augment the field of Cognitive Robotics by providing Aquila 2.0, a novel hi-performance software architecture utilising cross-platform, heterogeneous CPU-GPU modules loosely coupled with GUIs used for module management and data visualisation.

  • Aquila 2.0 software architecture for Cognitive Robotics
    2013 IEEE Third Joint International Conference on Development and Learning and Epigenetic Robotics (ICDL), 2013
    Co-Authors: Martin Peniak, Anthony Morse, Angelo Cangelosi
    Abstract:

    The modelling of the integration of various Cognitive skills and modalities requires complex and computationally intensive algorithms running in parallel while controlling high-performance systems. The distribution of processing across many computers has certainly advanced our software ecosystem and opened up research to new possibilities. While this was an essential move, we are aspiring to augment the field of Cognitive Robotics by providing Aquila 2.0, a novel hi-performance software architecture utilising cross-platform, heterogeneous CPU-GPU modules loosely coupled with GUIs used for module management and data visualisation.

  • 2012 special issue the grounding of higher order concepts in action and language a Cognitive Robotics model
    Neural Networks, 2012
    Co-Authors: Francesca Stramandinoli, Davide Marocco, Angelo Cangelosi
    Abstract:

    In this paper we present a neuro-robotic model that uses artificial neural networks for investigating the relations between the development of symbol manipulation capabilities and of sensorimotor knowledge in the humanoid robot iCub. We describe a Cognitive Robotics model in which the linguistic input provided by the experimenter guides the autonomous organization of the robot's knowledge. In this model, sequences of linguistic inputs lead to the development of higher-order concepts grounded on basic concepts and actions. In particular, we show that higher-order symbolic representations can be indirectly grounded in action primitives directly grounded in sensorimotor experiences. The use of recurrent neural network also permits the learning of higher-order concepts based on temporal sequences of action primitives. Hence, the meaning of a higher-order concept is obtained through the combination of basic sensorimotor knowledge. We argue that such a hierarchical organization of concepts can be a possible account for the acquisition of abstract words in Cognitive robots.

  • Mental practice and verbal instructions execution: A Cognitive Robotics study
    The 2012 International Joint Conference on Neural Networks (IJCNN), 2012
    Co-Authors: A. G. Di Nuovo, Angelo Cangelosi, D. Marocco, V. M. De La Cruz, S. Di Nuovo
    Abstract:

    Understanding the tight relationship that exists between mental imagery and motor activities (i.e. how images in the mind can influence movements and motor skills) has become a topic of interest and is of particular importance in domains in which improving those skills is crucial for obtaining better performance, such as in sports and rehabilitation. In this paper, using an embodied cognition approach and a Cognitive Robotics platform, we introduce initial results of an ongoing study that explores the impact linguistic stimuli could have in processes of mental imagery practice and subsequent motor execution and performance. Results are presented to show that the robot used, is able to “imagine” or “mentally” recall and accurately execute movements learned in previous training phases, strictly on the basis of the verbal commands issued. Further tests show that data obtained with “imagination” could be used to simulate “mental training” processes such as those that have been employed with human subjects in sports training, in order to enhance precision in the performance of new tasks, through the association of different verbal commands.

Maurice Pagnucco - One of the best experts on this subject based on the ideXlab platform.

  • learning and revision in Cognitive Robotics disassembly automation
    Robotics and Computer-integrated Manufacturing, 2015
    Co-Authors: Supachai Vongbunyong, Sami Kara, Maurice Pagnucco
    Abstract:

    Disassembly is a key step for an efficient treatment of end-of-life (EOL) products. A principle of Cognitive Robotics is implemented to address the problem regarding uncertainties and variations in the automatic disassembly process. In this article, advanced behaviour control based on two Cognitive abilities, namely learning and revision, are proposed. The knowledge related to the disassembly process of a particular model of product is learned by the Cognitive robotic agent (CRA) and will be implemented when the same model has been seen again. This knowledge is able to be used as a disassembly sequence plan (DSP) and disassembly process plan (DPP). The agent autonomously learns by reasoning throughout the process. In case of an unresolved condition, human assistance is given and the corresponding knowledge will be learned by demonstration. The process can be performed more efficiently by applying a revision strategy that optimises the operation plans. As a result, the performance of the process regarding time and level of autonomy are improved. The validation was done on various models of a case-study product, Liquid Crystal Display (LCD) screen. Actual implication of the learning and revision strategy in Cognitive robotic disassembly operation.Improvement of the process performance using learning and revision strategy.Disassembly sequence and process plans can be automatically generated by the CRA during the disassembly.

  • application of Cognitive Robotics in disassembly of products
    Cirp Annals-manufacturing Technology, 2013
    Co-Authors: Supachai Vongbunyong, Sami Kara, Maurice Pagnucco
    Abstract:

    Abstract Disassembly is a critical step to increase the value of end-of-life (EOL) products and to reduce the environmental footprint. Despite worldwide efforts, disassembly is still performed manually due to the uncertainty associated with the quality and the quantity of the returned EOL products. In this paper, a Cognitive Robotics based system is proposed to address this problem. The system is equipped with four Cognitive functions: reasoning, execution monitoring, learning and revision. The proposed system is tested using LCD screens. The results show that the system is flexible enough to deal with any product models without prior information.

  • a framework for using Cognitive Robotics in disassembly automation
    2012
    Co-Authors: Supachai Vongbunyong, Sami Kara, Maurice Pagnucco
    Abstract:

    Most of disassembly has been carried out manually due to the uncertainties associated with the quality and the quantity of the products returned. This has been a hindrance for automation of disassembly. In this research, the concept of “Cognitive Robotics” is proposed to address these problems. Cognitive Robotics is an autonomous robot equipped with Cognitive functionalities allowing the system to interact with the conditions occurring in a dynamic domain. This article proposes a framework of implementing Cognitive Robotics on a vision-based disassembly cell. Consequently, the system can deal with any product model in one product group regardless of their specific structure and geometrical detail.

Richard J. Duro - One of the best experts on this subject based on the ideXlab platform.

  • A Procedural Long Term Memory for Cognitive Robotics Optimizing Adaptive Learning in Dynamic Environments
    2020
    Co-Authors: Rodrigo Salgado, Francisco Bellas, Pilar Caamaño, B. Santos-diez, Richard J. Duro
    Abstract:

    This paper provides some insights into the advantages of using a Long-Term Memory (LTM) for optimizing the adaptive learning capabilities of a Cognitive robot in dynamic environments. Specifically, a procedural LTM that stores basic models and behaviours is included in the evolutionary-based Multilevel Darwinist Brain (MDB) Cognitive architecture. The memory system is based on learning error stability and instability to detect if a model is candidate to enter the LTM or to be recovered. A LTM replacement strategy has been developed that is based on context detection using functional comparison of the models’ response. The LTM elements are tested in theoretical functions and in a simulated example using the AIBO robot in a dynamic context with successful adaptive learning results. Keywords-Adaptive Learning, Cognitive Robotics, Evolutionary Computation, Dynamic Environments

  • dynamic learning in Cognitive Robotics through a procedural long term memory
    Evolving Systems, 2014
    Co-Authors: Francisco Bellas, Pilar Caamaño, Andres Faina, Richard J. Duro
    Abstract:

    Brain-like robotic approaches aim to reproduce the complex processes occurring within the biological brains to achieve a higher level of autonomy. One of the key aspects of these approaches is dynamic learning, that is, how to provide the Cognitive architectures that control de robot with adaptive learning capabilities. Several options have been considered in this line in the field of Cognitive Robotics, although the development of a proper memory system has provided the best practical results up to now. This work also follows this approach, seeking to show the advantages of using a Long-Term Memory (LTM) for optimizing the adaptive learning capabilities of a Cognitive robot in dynamic environments. Specifically, a procedural LTM that stores basic models and behaviours is included in the evolutionary-based Multilevel Darwinist Brain (MDB) Cognitive architecture. The LTM management system that has been developed to control when a model must be stored or replaced is presented here in detail. Moreover, a Short-Term Memory (STM) sub-system included in the MDB is also explained due to its strong relationship with the operation of the LTM. The LTM elements are tested in theoretical functions and in a simulated example using the AIBO robot in a dynamic context with successful adaptive learning results.

  • A procedural Long Term Memory for Cognitive Robotics
    2012 IEEE Conference on Evolving and Adaptive Intelligent Systems, 2012
    Co-Authors: Rodrigo Salgado, Francisco Bellas, Pilar Caamaño, B. Santos-diez, Richard J. Duro
    Abstract:

    This paper provides some insights into the advantages of using a Long-Term Memory (LTM) for optimizing the adaptive learning capabilities of a Cognitive robot in dynamic environments. Specifically, a procedural LTM that stores basic models and behaviours is included in the evolutionary-based Multilevel Darwinist Brain (MDB) Cognitive architecture. The memory system is based on learning error stability and instability to detect if a model is candidate to enter the LTM or to be recovered. A LTM replacement strategy has been developed that is based on context detection using functional comparison of the models' response. The LTM elements are tested in theoretical functions and in a simulated example using the AIBO robot in a dynamic context with successful adaptive learning results.

Heiko Wersing - One of the best experts on this subject based on the ideXlab platform.

  • ESANN - Recent Trends in Online Learning for Cognitive Robotics
    2020
    Co-Authors: Jochen J Steil, Heiko Wersing
    Abstract:

    We present a review of recent trends in Cognitive Robotics that deal with online learning approaches to the acquisition of knowledge, control strategies and behaviors of a Cognitive robot or agent. Along this line we focus on the topics of object recognition in Cognitive vision, tra- jectory learning and adaptive control of multi-DOF robots, task learning from demonstration, and general developmental approaches in Robotics. We argue for the relevance of online learning as a key ability for future intelligent robotic systems to allow flexible and adaptive behavior within a changing and unpredictable environment.

  • recent trends in online learning for Cognitive Robotics
    The European Symposium on Artificial Neural Networks, 2006
    Co-Authors: Jochen J Steil, Heiko Wersing
    Abstract:

    We present a review of recent trends in Cognitive Robotics that deal with online learning approaches to the acquisition of knowledge, control strategies and behaviors of a Cognitive robot or agent. Along this line we focus on the topics of object recognition in Cognitive vision, tra- jectory learning and adaptive control of multi-DOF robots, task learning from demonstration, and general developmental approaches in Robotics. We argue for the relevance of online learning as a key ability for future intelligent robotic systems to allow flexible and adaptive behavior within a changing and unpredictable environment.

Supachai Vongbunyong - One of the best experts on this subject based on the ideXlab platform.

  • learning and revision in Cognitive Robotics disassembly automation
    Robotics and Computer-integrated Manufacturing, 2015
    Co-Authors: Supachai Vongbunyong, Sami Kara, Maurice Pagnucco
    Abstract:

    Disassembly is a key step for an efficient treatment of end-of-life (EOL) products. A principle of Cognitive Robotics is implemented to address the problem regarding uncertainties and variations in the automatic disassembly process. In this article, advanced behaviour control based on two Cognitive abilities, namely learning and revision, are proposed. The knowledge related to the disassembly process of a particular model of product is learned by the Cognitive robotic agent (CRA) and will be implemented when the same model has been seen again. This knowledge is able to be used as a disassembly sequence plan (DSP) and disassembly process plan (DPP). The agent autonomously learns by reasoning throughout the process. In case of an unresolved condition, human assistance is given and the corresponding knowledge will be learned by demonstration. The process can be performed more efficiently by applying a revision strategy that optimises the operation plans. As a result, the performance of the process regarding time and level of autonomy are improved. The validation was done on various models of a case-study product, Liquid Crystal Display (LCD) screen. Actual implication of the learning and revision strategy in Cognitive robotic disassembly operation.Improvement of the process performance using learning and revision strategy.Disassembly sequence and process plans can be automatically generated by the CRA during the disassembly.

  • application of Cognitive Robotics in disassembly of products
    Cirp Annals-manufacturing Technology, 2013
    Co-Authors: Supachai Vongbunyong, Sami Kara, Maurice Pagnucco
    Abstract:

    Abstract Disassembly is a critical step to increase the value of end-of-life (EOL) products and to reduce the environmental footprint. Despite worldwide efforts, disassembly is still performed manually due to the uncertainty associated with the quality and the quantity of the returned EOL products. In this paper, a Cognitive Robotics based system is proposed to address this problem. The system is equipped with four Cognitive functions: reasoning, execution monitoring, learning and revision. The proposed system is tested using LCD screens. The results show that the system is flexible enough to deal with any product models without prior information.

  • a framework for using Cognitive Robotics in disassembly automation
    2012
    Co-Authors: Supachai Vongbunyong, Sami Kara, Maurice Pagnucco
    Abstract:

    Most of disassembly has been carried out manually due to the uncertainties associated with the quality and the quantity of the products returned. This has been a hindrance for automation of disassembly. In this research, the concept of “Cognitive Robotics” is proposed to address these problems. Cognitive Robotics is an autonomous robot equipped with Cognitive functionalities allowing the system to interact with the conditions occurring in a dynamic domain. This article proposes a framework of implementing Cognitive Robotics on a vision-based disassembly cell. Consequently, the system can deal with any product model in one product group regardless of their specific structure and geometrical detail.