NDLI: Incremental learning of gestures by imitation in a humanoid robot

Content Provider	IEEE Xplore Digital Library
Author	Calinon, S. Billard, A.
Copyright Year	2007
Description	Author affiliation: LASA Lab., Ecole Polytech. Fed. de Lausanne (EPFL), Lausanne, Switzerland (Calinon, S.; Billard, A.)
Abstract	We present an approach to teach incrementally human gestures to a humanoid robot. By using active teaching methods that puts the human teacher “in the loop” of the robot's learning, we show that the essential characteristics of a gesture can be efficiently transferred by interacting socially with the robot. In a first phase, the robot observes the user demonstrating the skill while wearing motion sensors. The motion of his/her two arms and head are recorded by the robot, projected in a latent space of motion and encoded probabilistically in a Gaussian Mixture Model (GMM). In a second phase, the user helps the robot refine its gesture by kinesthetic teaching, i.e. by grabbing and moving its arms throughout the movement to provide the appropriate scaffolds. To update the model of the gesture, we compare the performance of two incremental training procedures against a batch training procedure. We present experiments to show that different modalities can be combined efficiently to teach incrementally basketball officials' signals to a HOAP-3 humanoid robot.
Sponsorship	IEEE Robotics Autom. Soc.
Starting Page	255
Ending Page	262
File Size	2004027
Page Count	8
File Format	PDF
ISBN	9781595936172
ISSN	21672148
e-ISBN	9781595936172
DOI	10.1145/1228716.1228751
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-03-09
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Association for Computing Machinery, Inc. (ACM)
Subject Keyword	Actuators Gaussian Mixture Model Incremental learning RNA Abstracts Manipulators Programming by Demonstration Imitation Learning
Content Type	Text
Resource Type	Article

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