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 [BibTeX] [Marc21]
Learning from demonstrations with partially observable task parameters
Type of publication: Conference paper
Citation: Alizadeh_ICRA_2014
Publication status: Published
Booktitle: Proc. IEEE Intl Conf. on Robotics and Automation (ICRA)
Year: 2014
Month: June
Pages: 3309 - 3314
Publisher: IEEE
Location: Hong Kong
ISSN: 1050-4729
DOI: 10.1109/ICRA.2014.6907335
Abstract: Robot learning from demonstrations requires the robot to learn and adapt movements to new situations, often characterized by position and orientation of objects or landmarks in the robot’s environment. In the task-parameterized Gaussian mixture model framework, the movements are considered to be modulated with respect to a set of candidate frames of reference (coordinate systems) attached to a set of objects in the robot workspace. Following a similar approach, this paper addresses the problem of having missing candidate frames during the demonstrations and reproductions, which can happen in various situations such as visual occlusion, sensor unavailability, or tasks with a variable number of descriptive features. We study this problem with a dust sweeping task in which the robot requires to consider a variable amount of dust areas to clean for each reproduction trial.
Projects Idiap
Authors Alizadeh, T.
Calinon, Sylvain
Caldwell, D. G.
Added by: [UNK]
Total mark: 0
  • Alizadeh_ICRA_2014.pdf