Zobrazeno 1 - 10
of 142
pro vyhledávání: '"Inamura, Tetsunari"'
Publikováno v:
IEEE Transactions on Human-Machine Systems, 2022
This paper presents a new data-driven framework for analyzing periodic physical human-robot interaction (pHRI) in latent state space. To elaborate human understanding and/or robot control during pHRI, the model representing pHRI is critical. Recent d
Externí odkaz:
http://arxiv.org/abs/2106.08531
Autor:
Inamura, Tetsunari, Mizuchi, Yoshiaki
Common sense and social interaction related to daily-life environments are considerably important for autonomous robots, which support human activities. One of the practical approaches for acquiring such social interaction skills and semantic informa
Externí odkaz:
http://arxiv.org/abs/2005.00825
Robots are required to not only learn spatial concepts autonomously but also utilize such knowledge for various tasks in a domestic environment. Spatial concept represents a multimodal place category acquired from the robot's spatial experience inclu
Externí odkaz:
http://arxiv.org/abs/2002.07381
Humans interact in rich and diverse ways with the environment. However, the representation of such behavior by artificial agents is often limited. In this work we present \textit{motion concepts}, a novel multimodal representation of human actions in
Externí odkaz:
http://arxiv.org/abs/1903.02511
We propose a novel online learning algorithm, called SpCoSLAM 2.0, for spatial concepts and lexical acquisition with high accuracy and scalability. Previously, we proposed SpCoSLAM as an online learning algorithm based on unsupervised Bayesian probab
Externí odkaz:
http://arxiv.org/abs/1803.03481
In this paper, we propose an online learning algorithm based on a Rao-Blackwellized particle filter for spatial concept acquisition and mapping. We have proposed a nonparametric Bayesian spatial concept acquisition model (SpCoA). We propose a novel m
Externí odkaz:
http://arxiv.org/abs/1704.04664
Publikováno v:
Advanced Robotics, 31:3, 118-134, 2017
This paper describes a computational model, called the Dirichlet process Gaussian mixture model with latent joints (DPGMM-LJ), that can find latent tree structure embedded in data distribution in an unsupervised manner. By combining DPGMM-LJ and a pr
Externí odkaz:
http://arxiv.org/abs/1612.00305
In this paper, we propose a novel unsupervised learning method for the lexical acquisition of words related to places visited by robots, from human continuous speech signals. We address the problem of learning novel words by a robot that has no prior
Externí odkaz:
http://arxiv.org/abs/1602.01208
Autor:
Inamura, Tetsunari1,2 (AUTHOR) inamura@nii.ac.jp, Mizuchi, Yoshiaki1 (AUTHOR), Yamada, Hiroki1 (AUTHOR)
Publikováno v:
Advanced Robotics. Jun2021, Vol. 35 Issue 11, p697-703. 7p.
Akademický článek
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