NDLI: Learning task constraints for robot grasping using graphical models

Content Provider	IEEE Xplore Digital Library
Author	Song, D. Huebner, K. Kyrki, V. Kragic, D.
Copyright Year	2010
Description	Author affiliation: Lappeenranta University of Technology, Finland, Department of Information Technology (Kyrki, V.) \|\| KTH - Royal Institute of Technology, Stockholm, Sweden (Song, D.; Huebner, K.; Kragic, D.)
Abstract	This paper studies the learning of task constraints that allow grasp generation in a goal-directed manner. We show how an object representation and a grasp generated on it can be integrated with the task requirements. The scientific problems tackled are (i) identification and modeling of such task constraints, and (ii) integration between a semantically expressed goal of a task and quantitative constraint functions defined in the continuous object-action domains. We first define constraint functions given a set of object and action attributes, and then model the relationships between object, action, constraint features and the task using Bayesian networks. The probabilistic framework deals with uncertainty, combines a-priori knowledge with observed data, and allows inference on target attributes given only partial observations. We present a system designed to structure data generation and constraint learning processes that is applicable to new tasks, embodiments and sensory data. The application of the task constraint model is demonstrated in a goal-directed imitation experiment.
Starting Page	1579
Ending Page	1585
File Size	2132169
Page Count	7
File Format	PDF
ISBN	9781424466740
ISSN	21530866
e-ISBN	9781424466764
DOI	10.1109/IROS.2010.5649406
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-18
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Humans Robot sensing systems Bayesian methods Training Grasping Feature extraction
Content Type	Text
Resource Type	Article

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