AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
2023
Augello, Agnese; Gaglio, Salvatore; Infantino, Ignazio; Maniscalco, Umberto; Pilato, Giovanni; Vella, Filippo
Roboception and Adaptation in a Cognitive Robot Journal Article
In: Robotics and autonomous systems (Print), pp. 104400, 2023, ISSN: 0921-8890.
Abstract | Links | BibTeX | Tags: Cognitive Architectures, Humanoid Robots, Reinforcement Learning, Roboceptions, Sensor systems, Social Robots
@article{augelloRoboceptionAdaptationCognitive2023,
title = {Roboception and Adaptation in a Cognitive Robot},
author = { Agnese Augello and Salvatore Gaglio and Ignazio Infantino and Umberto Maniscalco and Giovanni Pilato and Filippo Vella},
doi = {10.1016/j.robot.2023.104400},
issn = {0921-8890},
year = {2023},
date = {2023-01-01},
journal = {Robotics and autonomous systems (Print)},
pages = {104400},
abstract = {In robotics, perception is usually oriented at understanding what is happening in the external world, while few works pay attention to what is occurring in the robot's body. In this work, we propose an artificial somatosensory system, embedded in a cognitive architecture, that enables a robot to perceive the sensations from its embodiment while executing a task. We called these perceptions roboceptions, and they let the robot act according to its own physical needs in addition to the task demands. Physical information is processed by the robot to behave in a balanced way, determining the most appropriate trade-off between the achievement of the task and its well being. The experiments show the integration of information from the somatosensory system and the choices that lead to the accomplishment of the task.},
keywords = {Cognitive Architectures, Humanoid Robots, Reinforcement Learning, Roboceptions, Sensor systems, Social Robots},
pubstate = {published},
tppubtype = {article}
}
In robotics, perception is usually oriented at understanding what is happening in the external world, while few works pay attention to what is occurring in the robot's body. In this work, we propose an artificial somatosensory system, embedded in a cognitive architecture, that enables a robot to perceive the sensations from its embodiment while executing a task. We called these perceptions roboceptions, and they let the robot act according to its own physical needs in addition to the task demands. Physical information is processed by the robot to behave in a balanced way, determining the most appropriate trade-off between the achievement of the task and its well being. The experiments show the integration of information from the somatosensory system and the choices that lead to the accomplishment of the task.
Augello, Agnese; Gaglio, Salvatore; Infantino, Ignazio; Maniscalco, Umberto; Pilato, Giovanni; Vella, Filippo
Roboception and adaptation in a cognitive robot Journal Article
In: Robotics and autonomous systems (Print), pp. 104400, 2023, ISSN: 0921-8890.
Abstract | Links | BibTeX | Tags: Cognitive Architectures, Humanoid Robots, Reinforcement Learning, Roboceptions, Sensor systems, Social Robots
@article{augello_roboception_2023,
title = {Roboception and adaptation in a cognitive robot},
author = {Agnese Augello and Salvatore Gaglio and Ignazio Infantino and Umberto Maniscalco and Giovanni Pilato and Filippo Vella},
url = {https://www.sciencedirect.com/science/article/pii/S0921889023000398},
doi = {10.1016/j.robot.2023.104400},
issn = {0921-8890},
year = {2023},
date = {2023-01-01},
journal = {Robotics and autonomous systems (Print)},
pages = {104400},
abstract = {In robotics, perception is usually oriented at understanding what is happening in the external world, while few works pay attention to what is occurring in the robotś body. In this work, we propose an artificial somatosensory system, embedded in a cognitive architecture, that enables a robot to perceive the sensations from its embodiment while executing a task. We called these perceptions roboceptions, and they let the robot act according to its own physical needs in addition to the task demands. Physical information is processed by the robot to behave in a balanced way, determining the most appropriate trade-off between the achievement of the task and its well being. The experiments show the integration of information from the somatosensory system and the choices that lead to the accomplishment of the task.},
keywords = {Cognitive Architectures, Humanoid Robots, Reinforcement Learning, Roboceptions, Sensor systems, Social Robots},
pubstate = {published},
tppubtype = {article}
}
In robotics, perception is usually oriented at understanding what is happening in the external world, while few works pay attention to what is occurring in the robotś body. In this work, we propose an artificial somatosensory system, embedded in a cognitive architecture, that enables a robot to perceive the sensations from its embodiment while executing a task. We called these perceptions roboceptions, and they let the robot act according to its own physical needs in addition to the task demands. Physical information is processed by the robot to behave in a balanced way, determining the most appropriate trade-off between the achievement of the task and its well being. The experiments show the integration of information from the somatosensory system and the choices that lead to the accomplishment of the task.
2020
Augello, Agnese; Infantino, Ignazio; Gaglio, Salvatore; Maniscalco, Umberto; Pilato, Giovanni; Vella, Filippo
An Artificial Soft Somatosensory System for a Cognitive Robot Proceedings Article
In: Proceedings - 4th IEEE International Conference on Robotic Computing, IRC 2020, pp. 319–326, Institute of Electrical and Electronics Engineers Inc., 2020, ISBN: 978-1-72815-237-0.
Abstract | Links | BibTeX | Tags: Cognitive Architectures, Reinforcement Learning, Robotics, Social Robots, Somatosensory Systems
@inproceedings{augelloArtificialSoftSomatosensory2020,
title = {An Artificial Soft Somatosensory System for a Cognitive Robot},
author = { Agnese Augello and Ignazio Infantino and Salvatore Gaglio and Umberto Maniscalco and Giovanni Pilato and Filippo Vella},
doi = {10.1109/IRC.2020.00058},
isbn = {978-1-72815-237-0},
year = {2020},
date = {2020-01-01},
booktitle = {Proceedings - 4th IEEE International Conference on Robotic Computing, IRC 2020},
pages = {319--326},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The paper proposes an artificial somatosensory system loosely inspired by human beings' biology and embedded in a cognitive architecture (CA). It enables a robot to receive the stimulation from its embodiment, and use these sensations, we called roboceptions, to behave according to both the external environment and the internal robot status. In such a way, the robot is aware of its body and able to interpret physical sensations can be more effective in the task while maintaining its well being. The robot's physiological urges are tightly bound to the specific physical state of the robot. Positive and negative physical information can, therefore, be processed and let the robot behave in a more realistic way adopting the right trade-off between the achievement of the task and the well-being of the robot. This goal has been achieved through a reinforcement learning approach. To test these statements we considered, as a test-bench, the execution of working performances with an SoftBank NAO robot that are modulated according its body well-being. textcopyright 2020 IEEE.},
keywords = {Cognitive Architectures, Reinforcement Learning, Robotics, Social Robots, Somatosensory Systems},
pubstate = {published},
tppubtype = {inproceedings}
}
The paper proposes an artificial somatosensory system loosely inspired by human beings' biology and embedded in a cognitive architecture (CA). It enables a robot to receive the stimulation from its embodiment, and use these sensations, we called roboceptions, to behave according to both the external environment and the internal robot status. In such a way, the robot is aware of its body and able to interpret physical sensations can be more effective in the task while maintaining its well being. The robot's physiological urges are tightly bound to the specific physical state of the robot. Positive and negative physical information can, therefore, be processed and let the robot behave in a more realistic way adopting the right trade-off between the achievement of the task and the well-being of the robot. This goal has been achieved through a reinforcement learning approach. To test these statements we considered, as a test-bench, the execution of working performances with an SoftBank NAO robot that are modulated according its body well-being. textcopyright 2020 IEEE.
Augello, Agnese; Infantino, Ignazio; Gaglio, Salvatore; Maniscalco, Umberto; Pilato, Giovanni; Vella, Filippo
An Artificial Soft Somatosensory System for a Cognitive Robot Proceedings Article
In: Proceedings - 4th IEEE International Conference on Robotic Computing, IRC 2020, pp. 319–326, Institute of Electrical and Electronics Engineers Inc., 2020, ISBN: 978-1-72815-237-0.
Abstract | Links | BibTeX | Tags: Cognitive Architectures, Reinforcement Learning, Robotics, Social Robots, Somatosensory Systems
@inproceedings{augello_artificial_2020,
title = {An Artificial Soft Somatosensory System for a Cognitive Robot},
author = {Agnese Augello and Ignazio Infantino and Salvatore Gaglio and Umberto Maniscalco and Giovanni Pilato and Filippo Vella},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099360477&doi=10.1109%2fIRC.2020.00058&partnerID=40&md5=87b4c20a11e6bca2f17e6cf2758353f8},
doi = {10.1109/IRC.2020.00058},
isbn = {978-1-72815-237-0},
year = {2020},
date = {2020-01-01},
booktitle = {Proceedings - 4th IEEE International Conference on Robotic Computing, IRC 2020},
pages = {319–326},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The paper proposes an artificial somatosensory system loosely inspired by human beings' biology and embedded in a cognitive architecture (CA). It enables a robot to receive the stimulation from its embodiment, and use these sensations, we called roboceptions, to behave according to both the external environment and the internal robot status. In such a way, the robot is aware of its body and able to interpret physical sensations can be more effective in the task while maintaining its well being. The robot's physiological urges are tightly bound to the specific physical state of the robot. Positive and negative physical information can, therefore, be processed and let the robot behave in a more realistic way adopting the right trade-off between the achievement of the task and the well-being of the robot. This goal has been achieved through a reinforcement learning approach. To test these statements we considered, as a test-bench, the execution of working performances with an SoftBank NAO robot that are modulated according its body well-being. © 2020 IEEE.},
keywords = {Cognitive Architectures, Reinforcement Learning, Robotics, Social Robots, Somatosensory Systems},
pubstate = {published},
tppubtype = {inproceedings}
}
The paper proposes an artificial somatosensory system loosely inspired by human beings' biology and embedded in a cognitive architecture (CA). It enables a robot to receive the stimulation from its embodiment, and use these sensations, we called roboceptions, to behave according to both the external environment and the internal robot status. In such a way, the robot is aware of its body and able to interpret physical sensations can be more effective in the task while maintaining its well being. The robot's physiological urges are tightly bound to the specific physical state of the robot. Positive and negative physical information can, therefore, be processed and let the robot behave in a more realistic way adopting the right trade-off between the achievement of the task and the well-being of the robot. This goal has been achieved through a reinforcement learning approach. To test these statements we considered, as a test-bench, the execution of working performances with an SoftBank NAO robot that are modulated according its body well-being. © 2020 IEEE.