Search Results for author: Tadahiro Taniguchi

Found 46 papers, 11 papers with code

Real-world Instance-specific Image Goal Navigation for Service Robots: Bridging the Domain Gap with Contrastive Learning

no code implementations15 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.

Contrastive Learning Deblurring +2

Lewis's Signaling Game as beta-VAE For Natural Word Lengths and Segments

1 code implementation8 Nov 2023 Ryo Ueda, Tadahiro Taniguchi

As a sub-discipline of evolutionary and computational linguistics, emergent communication (EC) studies communication protocols, called emergent languages, arising in simulations where agents communicate.

Representation Synthesis by Probabilistic Many-Valued Logic Operation in Self-Supervised Learning

no code implementations8 Sep 2023 Hiroki Nakamura, Masashi Okada, Tadahiro Taniguchi

Moreover, experiments on image retrieval using MNIST and PascalVOC showed that the representations of our method can be operated by OR and AND operations.

Image Classification Image Generation +5

Symbol emergence as interpersonal cross-situational learning: the emergence of lexical knowledge with combinatoriality

no code implementations27 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.

Metropolis-Hastings algorithm in joint-attention naming game: Experimental semiotics study

no code implementations31 May 2023 Ryota Okumura, Tadahiro Taniguchi, Yosinobu Hagiwara, Akira Taniguchi

By comparing human acceptance decisions of a partner's naming with acceptance probabilities computed in the MHNG, we tested whether human behavior is consistent with the MHNG theory.

Bayesian Inference

Recursive Metropolis-Hastings Naming Game: Symbol Emergence in a Multi-agent System based on Probabilistic Generative Models

no code implementations31 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.

Bayesian Inference

World Models and Predictive Coding for Cognitive and Developmental Robotics: Frontiers and Challenges

no code implementations14 Jan 2023 Tadahiro Taniguchi, Shingo Murata, Masahiro Suzuki, Dimitri Ognibene, Pablo Lanillos, Emre Ugur, Lorenzo Jamone, Tomoaki Nakamura, Alejandra Ciria, Bruno Lara, Giovanni Pezzulo

Therefore, in this paper, we clarify the definitions, relationships, and status of current research on these topics, as well as missing pieces of world models and predictive coding in conjunction with crucially related concepts such as the free-energy principle and active inference in the context of cognitive and developmental robotics.

Active Exploration based on Information Gain by Particle Filter for Efficient Spatial Concept Formation

no code implementations20 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.

Bayesian Inference Efficient Exploration +2

Brain-inspired probabilistic generative model for double articulation analysis of spoken language

no code implementations6 Jul 2022 Akira Taniguchi, Maoko Muro, Hiroshi Yamakawa, Tadahiro Taniguchi

This study proposes a PGM for a DAA hypothesis that can be realized in the brain based on the outcomes of several neuroscientific surveys.

Anatomy Sentence

Speak Like a Dog: Human to Non-human creature Voice Conversion

1 code implementation9 Jun 2022 Kohei Suzuki, Shoki Sakamoto, Tadahiro Taniguchi, Hirokazu Kameoka

This paper proposes a new voice conversion (VC) task from human speech to dog-like speech while preserving linguistic information as an example of human to non-human creature voice conversion (H2NH-VC) tasks.

Generative Adversarial Network Voice Conversion

Representation Uncertainty in Self-Supervised Learning as Variational Inference

no code implementations ICCV 2023 Hiroki Nakamura, Masashi Okada, Tadahiro Taniguchi

In this study, a novel self-supervised learning (SSL) method is proposed, which considers SSL in terms of variational inference to learn not only representation but also representation uncertainties.

Representation Learning Self-Supervised Learning +1

Hierarchical Path-planning from Speech Instructions with Spatial Concept-based Topometric Semantic Mapping

1 code implementation21 Mar 2022 Akira Taniguchi, Shuya Ito, Tadahiro Taniguchi

Navigation experiments using speech instruction with a waypoint demonstrated the performance improvement of path planning, WN-SPL by 0. 589, and reduced computation time by 7. 14 sec compared to conventional methods.

Multi-View Dreaming: Multi-View World Model with Contrastive Learning

no code implementations15 Mar 2022 Akira Kinose, Masashi Okada, Ryo Okumura, Tadahiro Taniguchi

In this paper, we propose Multi-View Dreaming, a novel reinforcement learning agent for integrated recognition and control from multi-view observations by extending Dreaming.

Contrastive Learning reinforcement-learning +1

Tactile-Sensitive NewtonianVAE for High-Accuracy Industrial Connector Insertion

no code implementations10 Mar 2022 Ryo Okumura, Nobuki Nishio, Tadahiro Taniguchi

An industrial connector insertion task requires submillimeter positioning and grasp pose compensation for a plug.

Vocal Bursts Intensity Prediction

DreamingV2: Reinforcement Learning with Discrete World Models without Reconstruction

no code implementations1 Mar 2022 Masashi Okada, Tadahiro Taniguchi

The present paper proposes a novel reinforcement learning method with world models, DreamingV2, a collaborative extension of DreamerV2 and Dreaming.

Contrastive Learning Model-based Reinforcement Learning +2

Unsupervised Multimodal Word Discovery based on Double Articulation Analysis with Co-occurrence cues

1 code implementation18 Jan 2022 Akira Taniguchi, Hiroaki Murakami, Ryo Ozaki, Tadahiro Taniguchi

The proposed method can acquire words and phonemes from speech signals using unsupervised learning and utilize object information based on multiple modalities-vision, tactile, and auditory-simultaneously.

Multiagent Multimodal Categorization for Symbol Emergence: Emergent Communication via Interpersonal Cross-modal Inference

no code implementations15 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.

StarGAN-VC+ASR: StarGAN-based Non-Parallel Voice Conversion Regularized by Automatic Speech Recognition

no code implementations10 Aug 2021 Shoki Sakamoto, Akira Taniguchi, Tadahiro Taniguchi, Hirokazu Kameoka

Although this method is powerful, it can fail to preserve the linguistic content of input speech when the number of available training samples is extremely small.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

Unsupervised Lexical Acquisition of Relative Spatial Concepts Using Spoken User Utterances

no code implementations16 Jun 2021 Rikunari Sagara, Ryo Taguchi, Akira Taniguchi, Tadahiro Taniguchi, Koosuke Hattori, Masahiro Hoguro, Taizo Umezaki

The experimental results show that relative spatial concepts and a phoneme sequence representing each concept can be learned under the condition that the robot does not know which located object is the reference object.


StarGAN-based Emotional Voice Conversion for Japanese Phrases

no code implementations5 Apr 2021 Asuka Moritani, Ryo Ozaki, Shoki Sakamoto, Hirokazu Kameoka, Tadahiro Taniguchi

Through subjective evaluation experiments, we evaluated the performance of our StarGAN-EVC system in terms of its ability to achieve EVC for Japanese phrases.

Voice Conversion

Double Articulation Analyzer with Prosody for Unsupervised Word and Phoneme Discovery

1 code implementation15 Mar 2021 Yasuaki Okuda, Ryo Ozaki, Tadahiro Taniguchi

The main contributions of this study are as follows: 1) We develop a probabilistic generative model for time series data including prosody that potentially has a double articulation structure; 2) We propose the Prosodic DAA by deriving the inference procedure for Prosodic HDP-HLM and show that Prosodic DAA can discover words directly from continuous human speech signals using statistical information and prosodic information in an unsupervised manner; 3) We show that prosodic cues contribute to word segmentation more in naturally distributed case words, i. e., they follow Zipf's law.

Language Modelling Time Series +1

A Whole Brain Probabilistic Generative Model: Toward Realizing Cognitive Architectures for Developmental Robots

no code implementations15 Mar 2021 Tadahiro Taniguchi, Hiroshi Yamakawa, Takayuki Nagai, Kenji Doya, Masamichi Sakagami, Masahiro Suzuki, Tomoaki Nakamura, Akira Taniguchi

This approach is based on two ideas: (1) brain-inspired AI, learning human brain architecture to build human-level intelligence, and (2) a probabilistic generative model(PGM)-based cognitive system to develop a cognitive system for developmental robots by integrating PGMs.

Hierarchical Bayesian Model for the Transfer of Knowledge on Spatial Concepts based on Multimodal Information

no code implementations11 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.

Dreaming: Model-based Reinforcement Learning by Latent Imagination without Reconstruction

no code implementations29 Jul 2020 Masashi Okada, Tadahiro Taniguchi

In the present paper, we propose a decoder-free extension of Dreamer, a leading model-based reinforcement learning (MBRL) method from pixels.

Contrastive Learning Data Augmentation +4

PlaNet of the Bayesians: Reconsidering and Improving Deep Planning Network by Incorporating Bayesian Inference

no code implementations1 Mar 2020 Masashi Okada, Norio Kosaka, Tadahiro Taniguchi

In this paper, we extend VI-MPC and PaETS, which have been originally introduced in previous literature, to address partially observable cases.

Bayesian Inference Continuous Control +3

Spatial Concept-Based Navigation with Human Speech Instructions via Probabilistic Inference on Bayesian Generative Model

1 code implementation18 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.

Decision Making

Autonomous Planning Based on Spatial Concepts to Tidy Up Home Environments with Service Robots

no code implementations10 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.

Domain-Adversarial and Conditional State Space Model for Imitation Learning

no code implementations31 Jan 2020 Ryo Okumura, Masashi Okada, Tadahiro Taniguchi

We experimentally evaluated the model predictive control performance via imitation learning for continuous control of sparse reward tasks in simulators and compared it with the performance of the existing SRL method.

Continuous Control Imitation Learning +2

Multi-person Pose Tracking using Sequential Monte Carlo with Probabilistic Neural Pose Predictor

no code implementations16 Sep 2019 Masashi Okada, Shinji Takenaka, Tadahiro Taniguchi

An important component of SMC, i. e., a proposal distribution, is designed as a probabilistic neural pose predictor, which can propose diverse and plausible hypotheses by incorporating epistemic uncertainty and heteroscedastic aleatoric uncertainty.

Pose Tracking

Variational Inference MPC for Bayesian Model-based Reinforcement Learning

no code implementations8 Jul 2019 Masashi Okada, Tadahiro Taniguchi

Probabilistic ensembles with trajectory sampling (PETS) is a leading type of MBRL, which employs Bayesian inference to dynamics modeling and model predictive control (MPC) with stochastic optimization via the cross entropy method (CEM).

Bayesian Inference Model-based Reinforcement Learning +5

Integration of Imitation Learning using GAIL and Reinforcement Learning using Task-achievement Rewards via Probabilistic Graphical Model

no code implementations3 Jul 2019 Akira Kinose, Tadahiro Taniguchi

In this paper, we present a new theory for integrating reinforcement and imitation learning by extending the probabilistic generative model framework for reinforcement learning, {\it plan by inference}.

General Knowledge Imitation Learning +2

Symbol Emergence as an Interpersonal Multimodal Categorization

no code implementations31 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.

Towards Understanding Language through Perception in Situated Human-Robot Interaction: From Word Grounding to Grammar Induction

no code implementations12 Dec 2018 Amir Aly, Tadahiro Taniguchi

Robots are widely collaborating with human users in diferent tasks that require high-level cognitive functions to make them able to discover the surrounding environment.

Improved and Scalable Online Learning of Spatial Concepts and Language Models with Mapping

3 code implementations9 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.

Symbol Emergence in Cognitive Developmental Systems: a Survey

no code implementations26 Jan 2018 Tadahiro Taniguchi, Emre Ugur, Matej Hoffmann, Lorenzo Jamone, Takayuki Nagai, Benjamin Rosman, Toshihiko Matsuka, Naoto Iwahashi, Erhan Oztop, Justus Piater, Florentin Wörgötter

However, the symbol grounding problem was originally posed to connect symbolic AI and sensorimotor information and did not consider many interdisciplinary phenomena in human communication and dynamic symbol systems in our society, which semiotics considered.

SERKET: An Architecture for Connecting Stochastic Models to Realize a Large-Scale Cognitive Model

1 code implementation4 Dec 2017 Tomoaki Nakamura, Takayuki Nagai, Tadahiro Taniguchi

Experimental results demonstrated that the model can be constructed by connecting modules, the parameters can be optimized as a whole, and they are comparable with the original models that we have proposed.

Spatial Concept Acquisition for a Mobile Robot that Integrates Self-Localization and Unsupervised Word Discovery from Spoken Sentences

no code implementations3 Feb 2016 Akira Taniguchi, Tadahiro Taniguchi, Tetsunari Inamura

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.

Multimodal Hierarchical Dirichlet Process-based Active Perception

1 code implementation1 Oct 2015 Tadahiro Taniguchi, Toshiaki Takano, Ryo Yoshino

We propose an MHDP-based active perception method that uses the information gain (IG) maximization criterion and lazy greedy algorithm.


Symbol Emergence in Robotics: A Survey

no code implementations29 Sep 2015 Tadahiro Taniguchi, Takayuki Nagai, Tomoaki Nakamura, Naoto Iwahashi, Tetsuya OGATA, Hideki Asoh

Humans can learn the use of language through physical interaction with their environment and semiotic communication with other people.

Nonparametric Bayesian Double Articulation Analyzer for Direct Language Acquisition from Continuous Speech Signals

no code implementations22 Jun 2015 Tadahiro Taniguchi, Ryo Nakashima, Shogo Nagasaka

In this paper, we develop a novel machine learning method called nonparametric Bayesian double articulation analyzer (NPB-DAA) that can directly acquire language and acoustic models from observed continuous speech signals.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +4

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