NDLI: Towards learning of safety knowledge from human demonstrations

Content Provider	IEEE Xplore Digital Library
Author	Ertle, P. Tokic, M. Cubek, R. Voos, H. Soffker, D.
Copyright Year	2012
Description	Author affiliation: University of Luxembourg, Faculté des Sciences, de la Technologie et de la Communication, L-1359, France (Voos, H.) \|\| University of Applied Sciences Ravensburg-Weingarten, ZAFH-Servicerobotik, 88250, Germany (Tokic, M.; Cubek, R.) \|\| University of Duisburg-Essen, 47057, Germany (Ertle, P.; Soffker, D.)
Abstract	Future autonomous service robots are intended to operate in open and complex environments. This in turn implies complications ensuring safe operation. The tenor of few available investigations is the need for dynamically assessing operational risks. Furthermore, a new kind of hazards being implicated by the robot's capability to manipulate the environment occurs: hazardous environmental object interactions. One of the open questions in safety research is integrating safety knowledge into robotic systems, enabling these systems behaving safety-conscious in hazardous situations. In this paper a safety procedure is described, in which learning of safety knowledge from human demonstration is considered. Within the procedure, a task is demonstrated to the robot, which observes object-to-object relations and labels situational data as commanded by the human. Based on this data, several supervised learning techniques are evaluated used for finally extracting safety knowledge. Results indicate that Decision Trees allow interesting opportunities.
Starting Page	5394
Ending Page	5399
File Size	617770
Page Count	6
File Format	PDF
ISBN	9781467317375
ISSN	21530858
e-ISBN	9781467317368
DOI	10.1109/IROS.2012.6385714
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-07
Publisher Place	Portugal
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robots Hazards Vectors Decision trees Iron Humans
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Computer Science Applications Computer Vision and Pattern Recognition Software

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