Online learning of humanoid robot kinematics under switching tools contexts

Lorenzo Jamone, Bruno Damas, Jose Santos-Victor, Atsuo Takanishi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

In this paper a novel approach to kinematics learning and task space control, under switching contexts, is presented. Such non-stationary contexts may appear in many robotic tasks: in particular, the changing of the context due to the use of tools with different lengths and shapes is herein studied. We model the robot forward kinematics as a multi-valued function, in which different outputs for the same input query are related to actual different hidden contexts. To do that, we employ IMLE, a recent online learning algorithm that fits an infinite mixture of linear experts to the online stream of training data. This algorithm can directly provide multi-valued regression in a online fashion, while having, for classic single-valued regression, a performance comparable to state-of-the-art online learning algorithms. The context varying forward kinematics is learned online through exploration, not relying on any kind of prior knowledge. Using the proposed approach, the robot can dynamically learn how to use different tools, without forgetting the kinematic mappings concerning previously manipulated tools. No information is given about such tool changes to the learning algorithm, nor any assumption is made about the tool kinematics. To our knowledge this is the most general and efficient approach to learning and control under discrete varying contexts. Some experimental results obtained on a high-dimensional simulated humanoid robot provide a strong support to our approach.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Robotics and Automation, ICRA 2013
Pages4811-4817
Number of pages7
DOIs
Publication statusPublished - 2013 Nov 14
Event2013 IEEE International Conference on Robotics and Automation, ICRA 2013 - Karlsruhe, Germany
Duration: 2013 May 62013 May 10

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

Conference2013 IEEE International Conference on Robotics and Automation, ICRA 2013
CountryGermany
CityKarlsruhe
Period13/5/613/5/10

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ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

Cite this

Jamone, L., Damas, B., Santos-Victor, J., & Takanishi, A. (2013). Online learning of humanoid robot kinematics under switching tools contexts. In 2013 IEEE International Conference on Robotics and Automation, ICRA 2013 (pp. 4811-4817). [6631263] (Proceedings - IEEE International Conference on Robotics and Automation). https://doi.org/10.1109/ICRA.2013.6631263