Publications

@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}

}