no code implementations • 15 Apr 2024 • Taichi Sakaguchi, Akira Taniguchi, Yoshinobu Hagiwara, Lotfi El Hafi, Shoichi Hasegawa, Tadahiro Taniguchi
To address this, we propose a novel method called Few-shot Cross-quality Instance-aware Adaptation (CrossIA), which employs contrastive learning with an instance classifier to align features between massive low- and few high-quality images.
no code implementations • 27 Jun 2023 • Yoshinobu Hagiwara, Kazuma Furukawa, Takafumi Horie, Akira Taniguchi, Tadahiro Taniguchi
We present a computational model for a symbol emergence system that enables the emergence of lexical knowledge with combinatoriality among agents through a Metropolis-Hastings naming game and cross-situational learning.
no code implementations • 31 May 2023 • Jun Inukai, Tadahiro Taniguchi, Akira Taniguchi, Yoshinobu Hagiwara
The main contributions of this paper are twofold: (1) we propose the recursive Metropolis-Hastings naming game (RMHNG) as an N-agent version of MHNG and demonstrate that RMHNG is an approximate Bayesian inference method for the posterior distribution over a latent variable shared by agents, similar to MHNG; and (2) we empirically evaluate the performance of RMHNG on synthetic and real image data, enabling multiple agents to develop and share a symbol system.
no code implementations • 20 Nov 2022 • Akira Taniguchi, Yoshiki Tabuchi, Tomochika Ishikawa, Lotfi El Hafi, Yoshinobu Hagiwara, Tadahiro Taniguchi
This study provides insights into the technical aspects of the proposed method, including active perception and exploration by the robot, and how the method can enable mobile robots to learn spatial concepts through active exploration.
1 code implementation • 24 May 2022 • Tadahiro Taniguchi, Yuto Yoshida, Akira Taniguchi, Yoshinobu Hagiwara
Instead, it is a game based on joint attention without explicit feedback.
no code implementations • 24 May 2022 • Kazuma Furukawa, Akira Taniguchi, Yoshinobu Hagiwara, Tadahiro Taniguchi
On the basis of the H2H-type Inter-MDM, we propose a naming game in the same way as the conventional Inter-MDM.
no code implementations • 15 Sep 2021 • Yoshinobu Hagiwara, Kazuma Furukawa, Akira Taniguchi, Tadahiro Taniguchi
(2) Function to improve the categorization accuracy in an agent via semiotic communication with another agent, even when some sensory modalities of each agent are missing.
no code implementations • 16 Mar 2021 • Yuki Katsumata, Akinori Kanechika, Akira Taniguchi, Lotfi El Hafi, Yoshinobu Hagiwara, Tadahiro Taniguchi
Using the spatial structure of various indoor environments as prior knowledge, the robot would construct the map more efficiently.
no code implementations • 11 Mar 2021 • Yoshinobu Hagiwara, Keishiro Taguchi, Satoshi Ishibushi, Akira Taniguchi, Tadahiro Taniguchi
This paper proposes a hierarchical Bayesian model based on spatial concepts that enables a robot to transfer the knowledge of places from experienced environments to a new environment.
1 code implementation • 18 Feb 2020 • Akira Taniguchi, Yoshinobu Hagiwara, Tadahiro Taniguchi, Tetsunari Inamura
The aim of this study is to enable a mobile robot to perform navigational tasks with human speech instructions, such as `Go to the kitchen', via probabilistic inference on a Bayesian generative model using spatial concepts.
no code implementations • 10 Feb 2020 • Akira Taniguchi, Shota Isobe, Lotfi El Hafi, Yoshinobu Hagiwara, Tadahiro Taniguchi
We evaluate the effectiveness of the proposed method by an experimental simulation that reproduces the conditions of the Tidy Up Here task of the World Robot Summit 2018 international robotics competition.
no code implementations • 31 May 2019 • Yoshinobu Hagiwara, Hiroyoshi Kobayashi, Akira Taniguchi, Tadahiro Taniguchi
In this paper, we describe a new computational model that represents symbol emergence in a two-agent system based on a probabilistic generative model for multimodal categorization.
3 code implementations • 9 Mar 2018 • Akira Taniguchi, Yoshinobu Hagiwara, Tadahiro Taniguchi, Tetsunari Inamura
We propose a novel online learning algorithm, called SpCoSLAM 2. 0, for spatial concepts and lexical acquisition with high accuracy and scalability.
5 code implementations • 15 Apr 2017 • Akira Taniguchi, Yoshinobu Hagiwara, Tadahiro Taniguchi, Tetsunari Inamura
We have proposed a nonparametric Bayesian spatial concept acquisition model (SpCoA).