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AR
2011
13 years 2 days ago
Learning, Generation and Recognition of Motions by Reference-Point-Dependent Probabilistic Models
This paper presents a novel method for learning object manipulation such as rotating an object or placing one object on another. In this method, motions are learned using referenc...
Komei Sugiura, Naoto Iwahashi, Hideki Kashioka, Sa...
IROS
2008
IEEE
113views Robotics» more  IROS 2008»
13 years 11 months ago
Motion recognition and generation by combining reference-point-dependent probabilistic models
— This paper presents a method to recognize and generate sequential motions for object manipulation such as placing one object on another or rotating it. Motions are learned usin...
Komei Sugiura, Naoto Iwahashi
ICML
2005
IEEE
14 years 5 months ago
Recognition and reproduction of gestures using a probabilistic framework combining PCA, ICA and HMM
This paper explores the issue of recognizing, generalizing and reproducing arbitrary gestures. We aim at extracting a representation that encapsulates only the key aspects of the ...
Sylvain Calinon, Aude Billard
CVPR
2005
IEEE
14 years 7 months ago
Hybrid Models for Human Motion Recognition
Probabilistic models have been previously shown to be efficient and effective for modeling and recognition of human motion. In particular we focus on methods which represent the h...
Claudio Fanti, Lihi Zelnik-Manor, Pietro Perona
ICRA
2006
IEEE
202views Robotics» more  ICRA 2006»
13 years 11 months ago
Primitive Communication based on Motion Recognition and Generation with Hierarchical Mimesis Model
— Communication skill is essential for social robots in various environments such as homes, offices, and hospitals, where the robots are expected to interact with humans. In thi...
Wataru Takano, Katsu Yamane, Tomomichi Sugihara, K...