Curiosity driven reinforcement learning for motion planning on humanoids

Frank, Mikhail, Leitner, Jurgen, Stollenga, Marijn, Forster, Alexander, & Schmidhuber, Jurgen (2014) Curiosity driven reinforcement learning for motion planning on humanoids. Frontiers in Neurorobotics, 7(25).

View at publisher (open access)


Most previous work on artificial curiosity (AC) and intrinsic motivation focuses on basic concepts and theory. Experimental results are generally limited to toy scenarios, such as navigation in a simulated maze, or control of a simple mechanical system with one or two degrees of freedom. To study AC in a more realistic setting, we embody a curious agent in the complex iCub humanoid robot. Our novel reinforcement learning (RL) framework consists of a state-of-the-art, low-level, reactive control layer, which controls the iCub while respecting constraints, and a high-level curious agent, which explores the iCub's state-action space through information gain maximization, learning a world model from experience, controlling the actual iCub hardware in real-time. To the best of our knowledge, this is the first ever embodied, curious agent for real-time motion planning on a humanoid. We demonstrate that it can learn compact Markov models to represent large regions of the iCub's configuration space, and that the iCub explores intelligently, showing interest in its physical constraints as well as in objects it finds in its environment.

Impact and interest:

7 citations in Web of Science®
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

67 since deposited on 18 Mar 2015
53 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 82595
Item Type: Journal Article
Refereed: Yes
Keywords: Artificial curiosity, Intrinsic motivation, Reinforcement learning, Humanoid, iCub, Embodied AI
DOI: 10.3389/fnbot.2013.00025
ISSN: 1662-5218
Divisions: Current > Research Centres > ARC Centre of Excellence for Robotic Vision
Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 Frank, Leitner, Stollenga, Förster and Schmidhuber.
Copyright Statement: This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Deposited On: 18 Mar 2015 22:38
Last Modified: 23 Mar 2015 04:16

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page