Original Research ARTICLE
Learning tactile skills through curious exploration
- 1Istituto Dalle Molle di Studi sull'Intelligenza Artificiale, Università della Svizzera Italiana, Lugano, Switzerland
- 2The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
We present curiosity-driven, autonomous acquisition of tactile exploratory skills on a biomimetic robot finger equipped with an array of microelectromechanical touch sensors. Instead of building tailored algorithms for solving a specific tactile task, we employ a more general curiosity-driven reinforcement learning approach that autonomously learns a set of motor skills in absence of an explicit teacher signal. In this approach, the acquisition of skills is driven by the information content of the sensory input signals relative to a learner that aims at representing sensory inputs using fewer and fewer computational resources. We show that, from initially random exploration of its environment, the robotic system autonomously develops a small set of basic motor skills that lead to different kinds of tactile input. Next, the system learns how to exploit the learned motor skills to solve supervised texture classification tasks. Our approach demonstrates the feasibility of autonomous acquisition of tactile skills on physical robotic platforms through curiosity-driven reinforcement learning, overcomes typical difficulties of engineered solutions for active tactile exploration and underactuated control, and provides a basis for studying developmental learning through intrinsic motivation in robots.
Keywords: active learning, biomimetic robotics, curiosity, intrinsic motivation, reinforcement learning, skill learning, tactile sensing
Citation: Pape L, Oddo CM, Controzzi M, Cipriani C, Förster A, Carrozza MC and Schmidhuber J (2012) Learning tactile skills through curious exploration. Front. Neurorobot. 6:6. doi: 10.3389/fnbot.2012.00006
Received: 16 March 2012; Accepted: 27 June 2012;
Published online: 23 July 2012.
Edited by:Robyn Grant, University of Sheffield, UK
Reviewed by:Nathan F. Lepora, University of Sheffield, UK
Benjamin Kuipers, University of Michigan, USA
Copyright © 2012 Pape, Oddo, Controzzi, Cipriani, Förster, Carrozza and Schmidhuber. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.
*Correspondence: Leo Pape, Istituto Dalle Molle di Studi sull'Intelligenza Artificiale, Università della Svizzera Italiana, Lugano, Switzerland. e-mail: email@example.com