Towards Self-controlled Robots Through Distributed Adaptive Control
Robots, as well as machine learning algorithms, have proven to be, unlike human beings, very sensitive to errors and failure. Artificial intelligence and machine learning are nowadays the main source of algorithms that drive cognitive robotics research. The advances in the fields have been huge during the last year, beating expert-human performance in video games, an achievement that was unthinkable a few years ago. Still, performance has been assessed by external measures not necessarily fit to the problem to solve, what lead to shameful failure on some specific tasks. We propose that the way to achieve human-like robustness in performance is to consider the self of the agent as the real source of self-evaluated error. This offers a solution to acting when information or resources are scarce and learning speed is important. This paper details our extension of the cognitive architecture DAC to control embodied agents and robots, through self-generated signals, from needs, drives, self-generated value and goals.
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