Sciweavers

Share
AR
2011

Learning, Generation and Recognition of Motions by Reference-Point-Dependent Probabilistic Models

8 years 1 months 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 reference-point-dependent probabilistic models, which can be used for the generation and recognition of motions. The method estimates (1) the reference point, (2) the intrinsic coordinate system type, which is the type of coordinate system intrinsic to a motion, and (3) the probabilistic model parameters of the motion that is considered in the intrinsic coordinate system. Motion trajectories are modeled by a hidden Markov model (HMM), and an HMM-based method using static and dynamic features is used for trajectory generation. The method was evaluated in physical experiments in terms of motion generation and recognition. In the experiments, users demonstrated the manipulation of puppets and toys so that the motions could be learned. A recognition accuracy of 90% was obtained for a test set of motions performed by thre...
Komei Sugiura, Naoto Iwahashi, Hideki Kashioka, Sa
Added 12 May 2011
Updated 12 May 2011
Type Journal
Year 2011
Where AR
Authors Komei Sugiura, Naoto Iwahashi, Hideki Kashioka, Satoshi Nakamura
Comments (0)
books