User observation & dataset collection for robot training

Personal robots have many things to learn and require a large quantity of data to learn them. Whether learning by demonstration, by trial and error, or collecting datasets for perception, robots will need to collect vast amounts of data without burdening the subjects. The parallels between gathering...

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Bibliographic Details
Published in2011 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI) pp. 217 - 218
Main Author Pantofaru, C.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2011
Subjects
Computer vision
Humans
Lasers
Measurement
Robot sensing systems
Training
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ISBN1467343935
9781467343930
ISSN2167-2121
DOI10.1145/1957656.1957739

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Summary:Personal robots have many things to learn and require a large quantity of data to learn them. Whether learning by demonstration, by trial and error, or collecting datasets for perception, robots will need to collect vast amounts of data without burdening the subjects. The parallels between gathering data for robot training and observing users during studies suggest the application of user study methodology as a basis for data collection methodology. Given the wide array of possible data, robotic platforms and algorithms, it is too early to set strict guidelines on collection practices. A clear set of guidelines, however, on how to report collection methodology and possible biases would benefit the community.
ISBN:1467343935
9781467343930
ISSN:2167-2121
DOI:10.1145/1957656.1957739