Multimodal Imitation using Self-learned Sensorimotor Representations

File Description SizeFormat 
IROS16_zambelli_demiris_stamped.pdfAccepted version3.96 MBAdobe PDFDownload
Title: Multimodal Imitation using Self-learned Sensorimotor Representations
Author(s): Zambelli, M
Demiris, Y
Item Type: Conference Paper
Abstract: Although many tasks intrinsically involve multiple modalities, often only data from a single modality are used to improve complex robots acquisition of new skills. We present a method to equip robots with multimodal learning skills to achieve multimodal imitation on-the-fly on multiple concurrent task spaces, including vision, touch and proprioception, only using self-learned multimodal sensorimotor relations, without the need of solving inverse kinematic problems or explicit analytical models formulation. We evaluate the proposed method on a humanoid iCub robot learning to interact with a piano keyboard and imitating a human demonstration. Since no assumptions are made on the kinematic structure of the robot, the method can be also applied to different robotic platforms.
Publication Date: 1-Dec-2016
Date of Acceptance: 1-Jul-2016
ISSN: 2153-0866
Publisher: IEEE
Journal / Book Title: Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on
Copyright Statement: ©2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor/Funder: Commission of the European Communities
Funder's Grant Number: 612139
Conference Name: IEEE/RSJ International Conference on Intelligent Robots and Systems
Publication Status: Published
Start Date: 2016-10-09
Finish Date: 2016-10-14
Conference Place: Daejeon, Korea
Appears in Collections:Faculty of Engineering
Electrical and Electronic Engineering

Items in Spiral are protected by copyright, with all rights reserved, unless otherwise indicated.

Creative Commons