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Conference papers

An Iterative Algorithm for Forward-Parameterized Skill Discovery

Abstract : We introduce COCOTTE (COnstrained Complexity Optimization Through iTerative merging of Experts), an iterative algorithm for discovering discrete, meaningful parameterized skills and learning explicit models of them from a set of behaviour examples. We show that forward-parameterized skills can be seen as smooth components of a locally smooth function and, framing the problem as the constrained minimization of a complexity measure, we propose an iterative algorithm to discover them. This algorithm fits well in the developmental robotics framework, as it does not require any external definition of a parameterized task, but discovers skills parameterized by the action from data. An application of our method to a simulated setup featuring a robotic arm interacting with an object is shown.
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Contributor : David Filliat Connect in order to contact the contributor
Submitted on : Friday, September 23, 2016 - 1:14:17 PM
Last modification on : Wednesday, May 11, 2022 - 3:20:02 PM


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  • HAL Id : hal-01370820, version 1


Adrien Matricon, David Filliat, Pierre-Yves Oudeyer. An Iterative Algorithm for Forward-Parameterized Skill Discovery. Sixth Joint IEEE International Conference Developmental Learning and Epigenetic Robotics (ICDL-EPIROB), Sep 2016, Cergy-Pontoise, France. ⟨hal-01370820⟩



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