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435 results on '"Taniguchi, Tadahiro"'

110. A Proposal of an Interactive Music Composition System Using Gibbs Sampler

Author

Shirai, Akira, Taniguchi, Tadahiro, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, and Jacko, Julie A., editor

Published
2011
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111. Developing a User Recommendation Engine on Twitter Using Estimated Latent Topics

Author

Koga, Hiroyuki, Taniguchi, Tadahiro, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, and Jacko, Julie A., editor

Published
2011
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115. Autonomous service robot for human-aware restock, straightening and disposal tasks in retail automation

Author

Garcia Ricardez, Gustavo A., primary, Uriguen Eljuri, Pedro M., additional, Kamemura, Yuta, additional, Yokota, Shiori, additional, Kugou, Noriyuki, additional, Asama, Yoshitatsu, additional, Wang, Ziyu, additional, Kumamoto, Hikaru, additional, Yoshimoto, Kotaro, additional, Chan, Wai Y., additional, Nagatani, Tomoki, additional, Tulathum, Pattaraporn, additional, Usawalertkamol, Bunyapon, additional, El Hafi, Lotfi, additional, Ikeuchi, Hiroki, additional, Yamamoto, Masaki, additional, Takamatsu, Jun, additional, Taniguchi, Tadahiro, additional, and Ogasawara, Tsukasa, additional

Published
2022
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116. Versatile cleaning service robot based on a mobile manipulator with tool switching for liquids and garbage removal in restrooms

Author

Wakayama, Tatsuhiro, primary, Fujiura, Eri, additional, Yamaguchi, Mari, additional, Yoshida, Naoki, additional, Inoue, Tomoaki, additional, Ikeuchi, Hiroki, additional, Yamamoto, Masaki, additional, El Hafi, Lotfi, additional, Garcia Ricardez, Gustavo A., additional, Takamatsu, Jun, additional, Taniguchi, Tadahiro, additional, and Ogasawara, Tsukasa, additional

Published
2022
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118. Imitation Learning from Unsegmented Human Motion Using Switching Autoregressive Model and Singular Vector Decomposition

Author

Taniguchi, Tadahiro, Iwahashi, Naoto, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Köppen, Mario, editor, Kasabov, Nikola, editor, and Coghill, George, editor

Published
2009
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124. Special issue on symbol emergence in robotics and cognitive systems (II)

Author

Taniguchi, Tadahiro, primary, Nagai, Takayuki, additional, Shimoda, Shingo, additional, Cangelosi, Angelo, additional, Demiris, Yiannis, additional, Matsuo, Yutaka, additional, Doya, Kenji, additional, Ogata, Tetsuya, additional, Jamone, Lorenzo, additional, Nagai, Yukie, additional, Ugur, Emre, additional, Mochihashi, Daichi, additional, Unno, Yuuya, additional, Okanoya, Kazuo, additional, and Hashimoto, Takashi, additional

Published
2022
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140. Implicit Estimation of Another's Intention Based on Modular Reinforcement Learning

Author

Taniguchi, Tadahiro, Ogawa, Kenji, and Sawaragi, Tetsuo

Abstract

We described a framework for implicitly estimating another's intentions based on modular reinforcement learning. We applied the framework to a truckpushing task by two agents as a concrete example. In the experiment, the Follower agent could perceive changes in the Leader's intentions and estimate his intentions without observing any explicit signs on any action outputs from the Leader. This demonstrated that autonomous agents can cooperatively achieve a task without any explicit communication. Self-enclosed autonomous agents can indirectly perceive the other's changes in intentions from changes in their surrounding environment. It is revealed that multiple internal models help an autonomous agent to achieve collaborative task. In the context of artificial intelligence, "symbol grounding problem" is considered as an important problem. The problem deals with how robots and people can relate their symbolic system to their physical and embodied experiences. The symbolic system mentioned here is also used in communication, usually. Takamuku et al. presented a system for lexicon acquisition through behavior learning which is based on a modified multimodule reinforcement. The robot in their work is able to automatically associate words to objects with various visual features based on similarities in features of dynamics[8]. At the same time, Taniguchi et al. described an integrative learning architecture for spike timingdependent plasticity (STDP) and the reinforcement learning schemata model (RLSM) [12, 11]. The learning architecture enables an autonomous robot to acquire behavioral concepts and signs representing the situation where the robot should initiate the behavior. They called this process "symbol emergence." The symbolic system plays a important role in human social communication.They also utilize modular learning architecture to describe the process of symbol organization. However, they treat bottomup organization of "explicit symbols," which is assumed to be used explicit communication. In many researches, "symbolic communication" means exchanging discrete signals. However, the essential point of symbolic communication is not such an externalized signs, but an adaptive formation of "interpretant" from the viewpoint of Peirce's semiotics. Therefore, we focus on the implicit communication and its bottom-up process of organization. However, the system we treated in this chapter is constrained to some extent. This framework for implicit estimates does not always work well. If the system does not satisfy the assumptions made in Section 4, the framework is not guaranteed to work. The Leader's policies are fixed when the Follower agent is learning its policies, predictors, and network connections in our framework. The model described in this chapter may not work in the simultaneous multi-agent reinforcement learning environment. We intend to take these into account in future work.

Published
2021

141. Editorial: Language and Robotics

Author

Taniguchi, Tadahiro, Horii, Takato, Hinaut, Xavier, Spranger, Michael, Mochihashi, Daichi, and Nagai, Takayuki

Subjects
Robotics and AI, symbol grounding in robotics, Editorial, multimodal communication, language acquisition by robots, deep learning for robotics, emergence of communication, symbol emergence in robotics, concept formation
Published
2021

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