Abstract

We present an approach for internally-guided learning in the context of a multi-task robot skill acquisition framework. More specifically, we focus on learning a parameterized distribution of robot movement primitives by using active intrinsically-motivated learning. We focus on the case where the learning process is initialized with human demonstrations, and refined through experiences. Such approach aims at combining experiential and observational learning. We demonstrate the effectiveness of our approach on a waste throwing task with a simulated 7-DoF Franka Emika robot.

Bibtex reference

@inproceedings{Kulak21ICRAWS,
	author="Kulak, T. and Calinon, S.",
	title="Intrinsically-Motivated Robot Learning of {B}ayesian Probabilistic Movement Primitives",
	booktitle="ICRA workshop ``Towards Curious Robots: Modern Approaches for Intrinsically-Motivated Intelligent Behavior''",
	year="2021",
	pages="1--4"
}

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