Manipulative Tasks Identification by Learning and Generalizing Hand Motions

Title:

Manipulative Tasks Identification by Learning and Generalizing Hand Motions

Authors:

Diego R. Faria [ website | social ]

Ricardo Martins [ website | social ]

Jorge Lobo [ website | social ]

Jorge Dias [ website | social ]

Abstract:

In this work is proposed an approach to learn patterns and recognize a manipulative task by the extracted features among multiples observations. The diversity of information such as hand motion, fingers flexure and object trajectory are important to represent a manipulative task. By using the relevant features is possible to generate a general form of the signals that represents a specific dataset of trials. The hand motion generalization process is achieved by polynomial regression. Later, given a new observation, it is performed a classification and identification of a task by using the learned features.

Publisher:

DoCEIS'11 - 2nd Doctoral Conference on Computing, Electrical and Industrial Systems

Date Published:

2011-02-01

Publisher website:

http://www.springerlink.com/content/00684g7705885736

DOI:

10.1007/978-3-642-19170-1_19

Alternative full-text PDF:

download full-text PDF via University of Coimbra

Keywords:

robotics, artificial perception, Bayesian modelling, grasping, probabilistic grid maps

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Manipulative Tasks Identification by Learning and Generalizing Hand Motions

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