Toward Self-Aware Robots

Toward Self-Aware Robots

Despite major progress in Robotics and AI, robots are still basically "zombies" repeatedly achieving actions and tasks without understanding what they are doing. Deep-Learning AI programs classify tremendous amounts of data without grasping the meaning of their inputs or outputs. We still...

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Bibliographic Details
Published in:Frontiers in robotics and AI Vol. 5; p. 88
Main Authors: Chatila, Raja, Renaudo, Erwan, Andries, Mihai, Chavez-Garcia, Ricardo-Omar, Luce-Vayrac, Pierre, Gottstein, Raphael, Alami, Rachid, Clodic, Aurélie, Devin, Sandra, Girard, Benoît, Khamassi, Mehdi
Format: Journal Article
Language:English
Published: Switzerland Frontiers Media S.A 13-08-2018
Subjects:
affordance
Artificial Intelligence
cognitive architecture
Cognitive science
Computer Science
decision-making
human-robot interaction
learning
Neural and Evolutionary Computing
Neuroscience
Robotics
Robotics and AI
self-awareness
human-robot interaction
planning
affordance
decision-making
cognitive architecture
learning
self-awareness
Markovian processes
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Summary:Despite major progress in Robotics and AI, robots are still basically "zombies" repeatedly achieving actions and tasks without understanding what they are doing. Deep-Learning AI programs classify tremendous amounts of data without grasping the meaning of their inputs or outputs. We still lack a genuine theory of the underlying principles and methods that would enable robots to understand their environment, to be cognizant of what they do, to take appropriate and timely initiatives, to learn from their own experience and to show that they know that they have learned and how. The rationale of this paper is that the understanding of its environment by an agent (the agent itself and its effects on the environment included) requires its self-awareness, which actually is itself emerging as a result of this understanding and the distinction that the agent is capable to make between its own mind-body and its environment. The paper develops along five issues: agent perception and interaction with the environment; learning actions; agent interaction with other agents-specifically humans; decision-making; and the cognitive architecture integrating these capacities.
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Edited by: Antonio Chella, Università degli Studi di Palermo, Italy
Reviewed by: Danilo Emilio De Rossi, Università degli Studi di Pisa, Italy; Owen E. Holland, University of Essex, United Kingdom
This article was submitted to Humanoid Robotics, a section of the journal Frontiers in Robotics and AI
ISSN:2296-9144
2296-9144
DOI:10.3389/frobt.2018.00088