A Curious Robot Learner for Interactive Goal-Babbling : Strategically Choosing What, How, When and from Whom to Learn.

Sao Mai Nguyen 1
1 Flowers - Flowing Epigenetic Robots and Systems
Inria Bordeaux - Sud-Ouest, U2IS - Unité d'Informatique et d'Ingénierie des Systèmes
Abstract : The challenges posed by robots operating in human environments on a daily basis and in the long-term point out the importance of adaptivity to changes which can be unforeseen at design time. Therefore, the robot must learn continuously in an open-ended, non-stationary and high dimensional space. It can not possibly explore all its environment to learn about everything within a life-time. To be useful and acquire skills, the robot must on the contrary be able to know which parts to sample and what kind of skills are interesting to learn. One way is to decide what to explore by oneself. Another way is to refer to a mentor. We name these two ways of collecting data sampling modes. The first sampling mode correspond to algorithms developed in the literature in order to autonomously drive the robot in interesting parts of the environment or useful kinds of skills. Such algorithms are called artificial curiosity or intrinsic motivation algorithms. The second sampling mode correspond to social guidance or imitation where the teacher indicates where to explore as well as where not to explore. Starting from the study of the relationships between these two concurrent methods, we ended up building an algorithmic architecture where relationships between the two modes intertwine into a hierarchical learning structure, called Socially Guided Intrinsic Motivation (SGIM).
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Submitted on : Monday, January 27, 2014 - 4:27:30 PM
Last modification on : Wednesday, July 3, 2019 - 10:48:04 AM
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Sao Mai Nguyen. A Curious Robot Learner for Interactive Goal-Babbling : Strategically Choosing What, How, When and from Whom to Learn.. Machine Learning [cs.LG]. Université Sciences et Technologies - Bordeaux I, 2013. English. ⟨tel-00936992⟩



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