記述言語：英語   掲載種別：研究論文（学術雑誌）

Real-world communicative signals—such as gestures, vocalizations, and facial expressions—are inherently continuous and subtle. Research on emergent communication has been advanced as a constructive approach to elucidate the emergence of symbol systems enabling communication among agents through interactions mediated by physical signals, namely, signs. However, research on emergent communication using decentralized Bayesian inference has predominantly relied on discrete symbolic representations, struggling to capture inherent subtleties. To address this limitation, we propose a probabilistic framework called multimodal Gaussian process (GP) emergent communication (MGPEmCom), which enables the emergence of continuous symbols among decentralized agents. This framework integrates GP latent variable models (GPLVMs) to describe multimodal sensory observations and employs the Metropolis–Hastings naming game (MHNG) for decentralized inference, allowing agents to jointly infer shared continuous signs without accessing one another’s internal states. We validate our approach with a referential game task in which a speaker uses an emergent continuous symbol to communicate a target object to a listener. Experimental results demonstrate that MGPEmCom outperforms existing methods, including Inter-MDM and VAE + REINFORCE, particularly in scenarios that require fine-grained distinctions and generalization to unknown objects. These results indicate that our method enables more expressive and generalizable symbol emergence. This work establishes a principled Bayesian approach to decentralized symbol emergence using continuous latent representations, offering a foundation for modeling communication applicable to unknown objects.

DOI： 10.1109/TCDS.2026.3661019

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Scopus

その他リンク： https://doi.org/10.1109/TCDS.2026.3661019

記述言語：英語   掲載種別：研究論文（学術雑誌）

Daily life instructions often include contextual cues related not to the object itself, but to its surrounding environment (e.g., objects placed around the target). Therefore, it is essential for service robots to interpret such surrounding information and translate it into action plans. Recently, there has been growing interest in using large language models (LLMs) to generate actions from instructions. Among these approaches, some studies explored leveraging semantic maps to better handle environmental context. However, previous studies face two limitations: 1) semantic maps are limited to object-level data, such as object class and position, and 2) since LLMs refer to semantic maps based on object classes, they cannot utilize the surrounding context, leading to misidentifications. To address them, we propose an action planning method that integrates the spatial concept model with LLMs. The spatial concept model categorizes observations and learns the parameters of probabilistic distributions. It then infers place names and object arrangements using Bayesian inference. By leveraging inference results as prompts, our method enables context-aware action planning. In the simulation, we designed object-search tasks using an open vocabulary within a household scenario in Gazebo. The robot received two types of instructions: 1) instructions that specify the target and its surrounding items, and 2) instructions that specify the target and its location. Our method achieved a success rate of over 0.8, outperforming baselines. On the other hand, real-world experiments revealed challenges in providing feedback when manipulation or detection failed. Project page: https://emergentsystemlabstudent.github.io/SpCoRAP/

DOI： 10.1109/ACCESS.2025.3647727

Kyutacar

Scopus

その他リンク： https://doi.org/10.1109/ACCESS.2025.3647727

記述言語：英語   掲載種別：研究論文（その他学術会議資料等）

Interpretability is a key requirement in deploying AI systems for safety-critical tasks such as anomaly detection. Brain-inspired AI offers a promising path toward interpretable models by aligning internal representations with known structures and functions of the brain. To address the challenge of developing interpretable anomaly detection systems, we propose a novel approach based on a spatial cognition model inspired by the hippocampal formation. Our model introduces three anomaly scores, each corresponding to a probabilistic variable associated with a specific subregion of the hippocampal formation: the lateral entorhinal cortex, the medial entorhinal cortex, and the cornu ammonis-1, respectively. Experiments in a simulated home environment, involving different types of anomalies such as missing observation data, pose estimation failures, and contextual anomalies (e.g., the kidnapped robot problem), show that some of the anomaly scores are most effective in detecting the type of anomaly that activates the corresponding hippocampal formation subregion in actual neuroscience studies. These results suggest that our hippocampal formation-inspired model is capable of anomaly detection and offers insights into the functional localization of internal processing that mirrors neuroscientific findings, thereby enhancing interpretability. Our approach lays the groundwork for integrating neuroscientific principles into designing more transparent and trustworthy AI systems.

DOI： 10.1007/978-981-95-4384-7_35

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Scopus

その他リンク： https://link.springer.com/content/pdf/10.1007/978-981-95-4384-7_35

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

Daily life support robots must interpret ambiguous verbal instructions involving demonstratives such as "Bring me that cup,"even when objects or users are out of the robot's view. Existing approaches to exophora resolution primarily rely on visual data and thus fail in real-world scenarios where the object or user is not visible. We propose Multimodal Interactive Exophora resolution with user Localization (MIEL), which is a multimodal exophora resolution framework leveraging sound source localization (SSL), semantic mapping, visual-language models (VLMs), and interactive questioning with GPT-4o. Our approach first constructs a semantic map of the environment and estimates candidate objects from a linguistic query with the user's skeletal data. SSL is utilized to orient the robot toward users who are initially outside its visual field, enabling accurate identification of user gestures and pointing directions. When ambiguities remain, the robot proactively interacts with the user, employing GPT-4o to formulate clarifying questions. Experiments in a real-world environment showed results that were approximately 1.3 times better when the user was visible to the robot and 2.0 times better when the user was not visible to the robot, compared to the methods without SSL and interactive questioning. The project website is https://emergentsystemlabstudent.github.io/MIEL/.

DOI： 10.1109/ro-man63969.2025.11217563

Kyutacar

Scopus

その他リンク： http://xplorestaging.ieee.org/ielx8/11217544/11217526/11217563.pdf?arnumber=11217563

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

Cybernetic avatars (CAs) are key components of an avatar-symbiotic society, enabling individuals to overcome physical limitations through virtual agents and robotic assistants. While semi-autonomous CAs intermittently require human teleoperation and supervision, the deployment of fully autonomous CAs remains a challenge. This study evaluates public perception and potential social impacts of fully autonomous CAs for physical support in daily life. To this end, we conducted a large-scale demonstration and survey during Avatar Land, a 19-day public event in Osaka, Japan, where fully autonomous robotic CAs, alongside semi-autonomous CAs, performed daily object retrieval tasks. Specifically, we analyzed responses from 2,285 visitors who engaged with various CAs, including a subset of 333 participants who interacted with fully autonomous CAs and shared their perceptions and concerns through a survey questionnaire. The survey results indicate interest in CAs for physical support in daily life and at work. However, concerns were raised regarding task execution reliability. In contrast, cost and human-like interaction were not dominant concerns. Project page: https://lotfielhafi.github.io/FACA-Survey/.

DOI： 10.1109/arso64737.2025.11124957

Kyutacar

Scopus

その他リンク： http://xplorestaging.ieee.org/ielx8/11124832/11124918/11124957.pdf?arnumber=11124957

記述言語：英語   掲載種別：研究論文（学術雑誌）

DOI： 10.48550/arXiv.2506.15468

Kyutacar

その他リンク： https://dblp.uni-trier.de/db/journals/corr/corr2506.html#abs-2506-15468

担当区分：筆頭著者,　責任著者   記述言語：英語   掲載種別：研究論文（その他学術会議資料等）

DOI： 10.51094/jxiv.1058

Kyutacar

その他リンク： https://jxiv.jst.go.jp/index.php/jxiv/preprint/view/1058

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

担当区分：責任著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

DOI： 10.48550/arXiv.2404.09645

Kyutacar

その他リンク： https://dblp.uni-trier.de/db/journals/corr/corr2404.html#abs-2404-09645

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

Robots that assist humans in their daily lives should be able to locate specific instances of objects in an environment that match a user's desired objects. This task is known as instance-specific image goal navigation (InstanceImageNav), which requires a model that can distinguish different instances of an object within the same class. A significant challenge in robotics is that when a robot observes the same object from various 3D viewpoints, its appearance may differ significantly, making it difficult to recognize and locate accurately. In this paper, we introduce a method called SimView, which leverages multi-view images based on a 3D semantic map of an environment and self-supervised learning using SimSiam to train an instance-identification model on-site. The effectiveness of our approach was validated using a photorealistic simulator, Habitat Matterport 3D, created by scanning actual home environments. Our results demonstrate a 1.7-fold improvement in task accuracy compared with contrastive language-image pre-training (CLIP), a pre-trained multimodal contrastive learning method for object searching. This improvement highlights the benefits of our proposed fine-tuning method in enhancing the performance of assistive robots in InstanceImageNav tasks. The project website is https://emergentsystemlabstudent.github.io/MultiViewRetrieve/.

DOI： 10.1109/iros58592.2024.10802697

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Scopus

その他リンク： http://xplorestaging.ieee.org/ielx8/10801246/10801290/10802697.pdf?arnumber=10802697

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

担当区分：筆頭著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）

Assisting individuals in their daily activities through autonomous mobile robots is a significant concern, especially for users without specialized knowledge. Specifically, the capability of a robot to navigate to destinations based on human speech instructions is crucial. Although robots can take different paths toward the same objective, the shortest path is not always the most suitable. A preferred approach would be to accommodate waypoint specifications flexibly for planning an improved alternative path even with detours. Furthermore, robots require real-time inference capabilities. In this sense, spatial representations include semantic, topological, and metric-level representations, each capturing different aspects of the environment. This study aimed to realize a hierarchical spatial representation using a topometric semantic map and path planning with speech instructions by including waypoints. Thus, we present a hierarchical path planning method called spatial concept-based topometric semantic mapping for hierarchical path planning (SpCoTMHP), which integrates place connectivity. This approach provides a novel integrated probabilistic generative model and fast approximate inferences with interactions among the hierarchy levels. A formulation based on “control as probabilistic inference” theoretically supports the proposed path planning algorithm. We conducted experiments in a home environment using the Toyota human support robot on the SIGVerse simulator and in a lab–office environment with the real robot Albert. Here, the user issues speech commands that specify the waypoint and goal, such as “Go to the bedroom via the corridor.” Navigation experiments were performed using speech instructions with a waypoint to demonstrate the performance improvement of the SpCoTMHP over the baseline hierarchical path planning method with heuristic path costs (HPP-I) in terms of the weighted success rate at which the robot reaches the closest target (0.590) and passes the correct waypoints. The computation time was significantly improved by 7.14 s with the SpCoTMHP than the baseline HPP-I in advanced tasks. Thus, hierarchical spatial representations provide mutually understandable instruction forms for both humans and robots, thus enabling language-based navigation.

DOI： 10.3389/frobt.2024.1291426

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Scopus

その他リンク： https://www.frontiersin.org/articles/10.3389/frobt.2024.1291426/full

記述言語：英語   掲載種別：研究論文（学術雑誌）

It remains difficult for mobile robots to continue accurate self-localization when they are suddenly teleported to a location that is different from their beliefs during navigation. Incorporating insights from neuroscience into developing a spatial cognition model for mobile robots may make it possible to acquire the ability to respond appropriately to changing situations, similar to living organisms. Recent neuroscience research has shown that during teleportation in rat navigation, neural populations of place cells in the cornu ammonis-3 region of the hippocampus, which are sparse representations of each other, switch discretely. In this study, we construct a spatial cognition model using brain reference architecture-driven development, a method for developing brain-inspired software that is functionally and structurally consistent with the brain. The spatial cognition model was realized by integrating the recurrent state—space model, a world model, with Monte Carlo localization to infer allocentric self-positions within the framework of neuro-symbol emergence in the robotics toolkit. The spatial cognition model, which models the cornu ammonis-1 and -3 regions with each latent variable, demonstrated improved self-localization performance of mobile robots during teleportation in a simulation environment. Moreover, it was confirmed that sparse neural activity could be obtained for the latent variables corresponding to cornu ammonis-3. These results suggest that spatial cognition models incorporating neuroscience insights can contribute to improving the self-localization technology for mobile robots. The project website is https://nakashimatakeshi.github.io/HF-IGL/.

DOI： 10.3389/fncom.2024.1398851

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Scopus

その他リンク： https://www.frontiersin.org/articles/10.3389/fncom.2024.1398851/full

記述言語：英語   掲載種別：研究論文（研究会，シンポジウム資料等）

記述言語：英語   掲載種別：研究論文（研究会，シンポジウム資料等）

記述言語：英語   掲載種別：研究論文（研究会，シンポジウム資料等）

記述言語：日本語   掲載種別：研究論文（学術雑誌）

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

Kyutacar

その他リンク： https://api.researchmap.jp/ataniguchi/research_projects/36727393

記述言語：英語   掲載種別：研究論文（学術雑誌）

Deep generative models (DGM) are increasingly employed in emergent communication systems. However, their application in multimodal data contexts is limited. This study proposes a novel model that combines multimodal DGM with the Metropolis-Hastings (MH) naming game, enabling two agents to focus jointly on a shared subject and develop common vocabularies. The model proves that it can handle multimodal data, even in cases of missing modalities. Integrating the MH naming game with multimodal variational autoencoders (VAE) allows agents to form perceptual categories and exchange signs within multimodal contexts. Moreover, fine-tuning the weight ratio to favor a modality that the model could learn and categorize more readily improved communication. Our evaluation of three multimodal approaches - mixture-of-experts (MoE), product-of-experts (PoE), and mixture-of-product-of-experts (MoPoE)–suggests an impact on the creation of latent spaces, the internal representations of agents. Our results from experiments with the MNIST + SVHN and Multimodal165 datasets indicate that combining the Gaussian mixture model (GMM), PoE multimodal VAE, and MH naming game substantially improved information sharing, knowledge formation, and data reconstruction.

DOI： 10.3389/frobt.2023.1290604

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Scopus

その他リンク： https://dblp.uni-trier.de/db/journals/firai/firai10.html#HoangTHT23

担当区分：筆頭著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）

Human infants acquire their verbal lexicon with minimal prior knowledge of language based on the statistical properties of phonological distributions and the co-occurrence of other sensory stimuli. This study proposes a novel fully unsupervised learning method for discovering speech units using phonological information as a distributional cue and object information as a co-occurrence cue. 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. The proposed method is based on the nonparametric Bayesian double articulation analyzer (NPB-DAA) discovering phonemes and words from phonological features, and multimodal latent Dirichlet allocation (MLDA) categorizing multimodal information obtained from objects. In an experiment, the proposed method showed higher word discovery performance than baseline methods. Words that expressed the characteristics of objects (i.e., words corresponding to nouns and adjectives) were segmented accurately. Furthermore, we examined how learning performance is affected by differences in the importance of linguistic information. Increasing the weight of the word modality further improved performance relative to that of the fixed condition.

DOI： 10.1109/TCDS.2023.3307555

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Scopus

その他リンク： https://dblp.uni-trier.de/db/journals/tamd/tamd15.html#TaniguchiMOT23

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

Active semantic mapping is essential for service robots to quickly capture both the map of an environment and its spatial meaning, while also minimizing the burden on users during robot operation and data collection. SpCoSLAM, a method of semantic mapping with place categorization and simultaneous localization and mapping (SLAM), is well suited to environmental adaptation, as it is not limited to predefined labels. However, SpCoSLAM presents two issues that increase the burden on users: 1) users struggle to efficiently determine a destination for the robot's quick adaptation, and 2) providing instructions to the robot becomes repetitive and cumbersome. To address these challenges, we propose Active-SpCoSLAM, which enables the robot to actively explore uncharted areas and employs CLIP for image captioning to provide a flexible vocabulary that replaces human instructions. The robot determines its actions by calculating information gain integrated from both semantics and SLAM uncertainties. We conducted experiments in a simulated environment, comparing the proposed method to other methods in terms of efficiency and applicability to object discovery tasks. Additionally, we tested the proposed method, which combines user instruction and CLIP, in a real environment. Our results demonstrated that the robot explored its environment with approximately five fewer iterations and 11 minutes faster compared to the case of random exploration. Moreover, our method achieved a higher success rate in object discovery tasks during earlier stages of learning compared to other methods. In conclusion, the proposed method rapidly covers an environment while gathering useful data for object discovery tasks, thus reducing the burden on users and enhancing the robot's adaptability. The project website is https://tomochika-ishikawa.github.io/Active-SpCoSLAM/.

DOI： 10.1109/smc53992.2023.10394143

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Scopus

その他リンク： http://xplorestaging.ieee.org/ielx7/10391856/10393862/10394143.pdf?arnumber=10394143

記述言語：英語   掲載種別：研究論文（学術雑誌）

Constructive studies on symbol emergence systems seek to investigate computational models that can better explain human language evolution, the creation of symbol systems, and the construction of internal representations. Specifically, emergent communication aims to formulate a computational model that enables agents to build efficient sign systems and internal representations. This study provides a new model for emergent communication, which is based on a probabilistic generative model (PGM) instead of a discriminative model based on deep reinforcement learning. We define the Metropolis-Hastings (MH) naming game by generalizing previously proposed models. It is not a referential game with explicit feedback, as assumed by many emergent communication studies. Instead, it is a game based on joint attention without explicit feedback. Mathematically, the MH naming game is proved to be a type of MH algorithm for an integrative PGM that combines two agents that play the naming game. From this viewpoint, symbol emergence is regarded as decentralized Bayesian inference, and semiotic communication is regarded as inter-personal cross-modal inference. This notion leads to the collective predictive coding hypothesis regarding language evolution and, in general, the emergence of symbols. We also propose the inter-Gaussian mixture model (GMM)+ variational autoencoder (VAE), a deep generative model for emergent communication based on the MH naming game. In this model, two agents create internal representations and categories and share signs (i.e. names of objects) from raw visual images observed from different viewpoints. The model has been validated on MNIST and Fruits 360 datasets. Experimental findings demonstrate that categories are formed from real images observed by agents, and signs are correctly shared across agents by successfully utilizing both of the observations of agents via the MH naming game. Furthermore, scholars verified that visual images were recalled from signs uttered by agents. Notably, emergent communication without supervision and reward feedback improved the performance of the unsupervised representation learning of agents.

DOI： 10.1080/01691864.2023.2260856

Kyutacar

Scopus

その他リンク： https://dblp.uni-trier.de/db/journals/ar/ar37.html#TaniguchiYMHTH23

担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（学術雑誌）

In building artificial intelligence (AI) agents, referring to how brains function in real environments can accelerate development by reducing the design space. In this study, we propose a probabilistic generative model (PGM) for navigation in uncertain environments by integrating the neuroscientific knowledge of hippocampal formation (HF) and the engineering knowledge in robotics and AI, namely, simultaneous localization and mapping (SLAM). We follow the approach of brain reference architecture (BRA) (Yamakawa, 2021) to compose the PGM and outline how to verify the model. To this end, we survey and discuss the relationship between the HF findings and SLAM models. The proposed hippocampal formation-inspired probabilistic generative model (HF-PGM) is designed to be highly consistent with the anatomical structure and functions of the HF. By referencing the brain, we elaborate on the importance of integration of egocentric/allocentric information from the entorhinal cortex to the hippocampus and the use of discrete-event queues.

DOI： 10.1016/j.neunet.2022.04.001

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Scopus

CiNii Research

PubMed

その他リンク： https://dblp.uni-trier.de/db/journals/nn/nn151.html#TaniguchiFY22

記述言語：英語   掲載種別：研究論文（学術雑誌）

Kyutacar

その他リンク： https://arxiv.org/abs/2103.08183

記述言語：英語   掲載種別：研究論文（学術雑誌）

This paper describes a computational model of multiagent multimodal categorization that realizes emergent communication. We clarify whether the computational model can reproduce the following functions in a symbol emergence system, comprising two agents with different sensory modalities playing a naming game. (1) Function for forming a shared lexical system that comprises perceptual categories and corresponding signs, formed by agents through individual learning and semiotic communication. (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. (3) Function that an agent infers unobserved sensory information based on a sign sampled from another agent in the same manner as cross-modal inference. We propose an interpersonal multimodal Dirichlet mixture (Inter-MDM), which is derived by dividing an integrative probabilistic generative model, which is obtained by integrating two Dirichlet mixtures (DMs). The Markov chain Monte Carlo algorithm realizes emergent communication. The experimental results demonstrated that Inter-MDM enables agents to form multimodal categories and appropriately share signs between agents. It is shown that emergent communication improves categorization accuracy, even when some sensory modalities are missing. Inter-MDM enables an agent to predict unobserved information based on a shared sign.

DOI： 10.1080/01691864.2022.2029721

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Scopus

その他リンク： https://www.tandfonline.com/doi/pdf/10.1080/01691864.2022.2029721

記述言語：英語   掲載種別：研究論文（学術雑誌）

Using the spatial structure of various indoor environments as prior knowledge, the robot would construct the map more efficiently. Autonomous mobile robots generally apply simultaneous localization and mapping (SLAM) methods to understand the reachable area in newly visited environments. However, conventional mapping approaches are limited by only considering sensor observation and control signals to estimate the current environment map. This paper proposes a novel SLAM method, map completion network-based SLAM (MCN-SLAM), based on a probabilistic generative model incorporating deep neural networks for map completion. These map completion networks are primarily trained in the framework of generative adversarial networks (GANs) to extract the global structure of large amounts of existing map data. We show in experiments that the proposed method can estimate the environment map 1.3 times better than the previous SLAM methods in the situation of partial observation.

DOI： 10.1080/01691864.2022.2029762

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Scopus

CiNii Research

その他リンク： https://www.tandfonline.com/doi/pdf/10.1080/01691864.2022.2029762

記述言語：英語   掲載種別：研究論文（学術雑誌）

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. The transfer of knowledge based on spatial concepts is modeled as the calculation process of the posterior distribution based on the observations obtained in each environment with the parameters of spatial concepts generalized to environments as prior knowledge. We conducted experiments to evaluate the generalization performance of spatial knowledge for general places such as kitchens and the adaptive performance of spatial knowledge for unique places such as `Emma's room' in a new environment. In the experiments, the accuracies of the proposed method and conventional methods were compared in the prediction task of location names from an image and a position, and the prediction task of positions from a location name. The experimental results demonstrated that the proposed method has a higher prediction accuracy of location names and positions than the conventional method owing to the transfer of knowledge.

DOI： 10.1080/01691864.2021.2004224

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Scopus

CiNii Research

その他リンク： https://www.tandfonline.com/doi/pdf/10.1080/01691864.2021.2004224

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

In this paper, we address the task of rearranging items with a robot. A rearrangement task is challenging because it requires us to solve the following issues: determine how to pick the items and plan how and where to place the items. In our previous work, we proposed to solve a rearrangement task by combining the symbolic and motion planners using a Motion Feasibility Checker (MFC) and a Monte Carlo Tree Search (MCTS). The MCTS searches for the goal while it collaborates with the MFC to accept or reject instructions. We could solve the rearrangement task, but one drawback is the time it takes to find a solution. In this study, we focus on quickly accepting or rejecting tentative instructions obtained from an MCTS. We propose using a Neural Network-based Motion Feasibility Checker (NN-MFC), a Fully Connected Neural Network trained with data obtained from the MFC. This NN-MFC quickly decides if the instruction is valid or not, reducing the time the MCTS uses to find a solution to the task. The NN-MFC determines the validity of the instruction based on the initial and target poses of the item. Before the final execution of the instructions, we re-validate the instructions with the MFC as a confirmation before execution. We tested the proposed method in a simulation environment by doing an item rearrangement task in a convenience store setup.

DOI： 10.1109/SII52469.2022.9708602

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Scopus

その他リンク： https://dblp.uni-trier.de/rec/conf/sii/2022

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

This study aims to further develop the task of novel view synthesis by generative adversarial networks (GAN). The goal of novel view synthesis is to, given one or more input images, synthesize images of the same target content but from different viewpoints. Previous research showed that the unsupervised learning model HoloGAN achieved high performance in generating images from different viewpoints. However, HoloGAN is less capable of specifying the target content to generate and is difficult to train due to high data requirements. Therefore, this study proposes two approaches to improve the current limitations of HoloGAN and make it suitable for the task of novel view synthesis. The first approach reuses the encoder network of HoloGAN to get the corresponding latent vectors of the image contents to specify the target content of the generated images. The second approach introduces an auto-encoder architecture to HoloGAN so that more viewpoints can be generated correctly. The experiment results indicate that the first approach is efficient in specifying a target content. Meanwhile, the second approach method helps HoloGAN to learn a richer range of viewpoints but is not compatible with the first approach. The combination of these two approaches and their application to service robotics are discussed in conclusion.

DOI： 10.1109/SII52469.2022.9708823

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Scopus

その他リンク： https://dblp.uni-trier.de/rec/conf/sii/2022

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

This study proposes a learning method for relative and absolute spatial concepts using spoken user utterances. Service robots that assist humans in performing daily activities must be able to learn and understand spatial concepts and their linguistic representations. Spatial concepts are divided into two types: relative spatial concepts (e.g., front and right) and absolute spatial concepts (e.g., kitchen and corridor). Robots are required to learn both types of concepts. Therefore, we propose a method by which a robot can learn both relative and absolute spatial concepts without prior knowledge of words. The method is formulated using a probabilistic model to mutually complement the uncertainty of locations and spoken user utterances. The experimental results demonstrate that both relative and absolute spatial concepts can be learned under the condition that the robot does not know which type of concept is uttered. In addition, we show that spoken user utterances are segmented with high accuracy.

DOI： 10.1109/GCCE53005.2021.9621937

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Scopus

その他リンク： https://dblp.uni-trier.de/rec/conf/gcce/2021

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

記述言語：英語   掲載種別：研究論文（学術雑誌）

The installation of remotely-operated service robots in the environments of our daily life (including offices, homes, and hospitals) can improve work-from-home policies and enhance the quality of t...

DOI： 10.1080/01691864.2021.1928552

Kyutacar

Scopus

CiNii Research

その他リンク： https://www.tandfonline.com/doi/pdf/10.1080/01691864.2021.1928552

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

As service robots are becoming essential to support aging societies, teaching them how to perform general service tasks is still a major challenge preventing their deployment in daily-life environments. In addition, developing an artificial intelligence for general service tasks requires bottom-up, unsupervised approaches to let the robots learn from their own observations and interactions with the users. However, compared to the top-down, supervised approaches such as deep learning where the extent of the learning is directly related to the amount and variety of the pre-existing data provided to the robots, and thus relatively easy to understand from a human perspective, the learning status in bottom-up approaches is by their nature much harder to appreciate and visualize. To address these issues, we propose a teaching system for multimodal object categorization by human-robot interaction through Mixed Reality (MR) visualization. In particular, our proposed system enables a user to monitor and intervene in the robot's object categorization process based on Multimodal Latent Dirichlet Allocation (MLDA) to solve unexpected results and accelerate the learning. Our contribution is twofold by 1) describing the integration of a service robot, MR interactions, and MLDA object categorization in a unified system, and 2) proposing an MR user interface to teach robots through intuitive visualization and interactions.

DOI： 10.1109/IEEECONF49454.2021.9382607

Kyutacar

Scopus

その他リンク： https://dblp.uni-trier.de/conf/sii/2021

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（学術雑誌）

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 including vision, speech-language, and self-position. 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. Specifically, path planning was formalized as the maximization of probabilistic distribution on the path-trajectory under speech instruction, based on a control-as-inference framework. Furthermore, we described the relationship between probabilistic inference based on the Bayesian generative model and control problem including reinforcement learning. We demonstrated path planning based on human instruction using acquired spatial concepts to verify the usefulness of the proposed approach in the simulator and in real environments. Experimentally, places instructed by the user's speech commands showed high probability values, and the trajectory toward the target place was correctly estimated. Our approach, based on probabilistic inference concerning decision-making, can lead to further improvement in robot autonomy.

DOI： 10.1080/01691864.2020.1817777

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Scopus

CiNii Research

その他リンク： https://www.tandfonline.com/doi/pdf/10.1080/01691864.2020.1817777

担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（学術雑誌）

<jats:title>Abstract</jats:title><jats:p>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 probabilistic model that integrates multimodal place categorization, lexical acquisition, and SLAM. However, our original algorithm had limited estimation accuracy owing to the influence of the early stages of learning, and increased computational complexity with added training data. Therefore, we introduce techniques such as fixed-lag rejuvenation to reduce the calculation time while maintaining an accuracy higher than that of the original algorithm. The results show that, in terms of estimation accuracy, the proposed algorithm exceeds the original algorithm and is comparable to batch learning. In addition, the calculation time of the proposed algorithm does not depend on the amount of training data and becomes constant for each step of the scalable algorithm. Our approach will contribute to the realization of long-term spatial language interactions between humans and robots.</jats:p>

DOI： 10.1007/s10514-020-09905-0

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Scopus

CiNii Research

その他リンク： http://www.scopus.com/inward/record.url?eid=2-s2.0-85079468644&partnerID=MN8TOARS

記述言語：日本語   掲載種別：研究論文（研究会，シンポジウム資料等）

DOI： 10.11517/pjsai.JSAI2020.0_2M4OS3a05

Kyutacar

その他リンク： https://doi.org/10.11517/pjsai.JSAI2020.0_2M4OS3a05

記述言語：英語   掲載種別：研究論文（学術雑誌）

Human–robot interaction during general service tasks in home or retail environment has been proven challenging, partly because (1) robots lack high-level context-based cognition and (2) humans cannot intuit the perception state of robots as they can for other humans. To solve these two problems, we present a complete robot system that has been given the highest evaluation score at the Customer Interaction Task of the Future Convenience Store Challenge at the World Robot Summit 2018, which implements several key technologies: (1) a hierarchical spatial concepts formation for general robot task planning and (2) a mixed reality interface to enable users to intuitively visualize the current state of the robot perception and naturally interact with it. The results obtained during the competition indicate that the proposed system allows both non-expert operators and end users to achieve human–robot interactions in customer service environments. Furthermore, we describe a detailed scenario including employee operation and customer interaction which serves as a set of requirements for service robots and a road map for development. The system integration and task scenario described in this paper should be helpful for groups facing customer interaction challenges and looking for a successfully deployed base to build on.

DOI： 10.1080/01691864.2019.1694068

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Scopus

その他リンク： https://doi.org/10.1080/01691864.2019.1694068

記述言語：英語   掲載種別：研究論文（学術雑誌）

This paper describes a framework for the development of an integrative cognitive system based on probabilistic generative models (PGMs) called Neuro-SERKET. Neuro-SERKET is an extension of SERKET, which can compose elemental PGMs developed in a distributed manner and provide a scheme that allows the composed PGMs to learn throughout the system in an unsupervised way. In addition to the head-to-tail connection supported by SERKET, Neuro-SERKET supports tail-to-tail and head-to-head connections, as well as neural network-based modules, i.e., deep generative models. As an example of a Neuro-SERKET application, an integrative model was developed by composing a variational autoencoder (VAE), a Gaussian mixture model (GMM), latent Dirichlet allocation (LDA), and automatic speech recognition (ASR). The model is called VAE + GMM + LDA + ASR. The performance of VAE + GMM + LDA + ASR and the validity of Neuro-SERKET were demonstrated through a multimodal categorization task using image data and a speech signal of numerical digits.

DOI： 10.1007/s00354-019-00084-w

Kyutacar

Scopus

その他リンク： https://dblp.uni-trier.de/db/journals/ngc/ngc38.html#TaniguchiNSKHTH20

記述言語：英語   掲載種別：研究論文（学術雑誌）

DOI： 10.3389/frobt.2019.00134

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Scopus

その他リンク： https://doi.org/10.3389/frobt.2019.00134

担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（学術雑誌）

DOI： 10.1016/j.robot.2017.10.013

Kyutacar

Scopus

その他リンク： http://dblp.uni-trier.de/db/journals/ras/ras99.html#journals/ras/TaniguchiTI18

担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

DOI： 10.1109/IROS.2017.8202243

Kyutacar

Scopus

arXiv

その他リンク： http://dblp.uni-trier.de/db/conf/iros/iros2017.html#conf/iros/TaniguchiHTI17

担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（学術雑誌）

DOI： 10.3389/fnbot.2017.00066

Kyutacar

その他リンク： http://dblp.uni-trier.de/db/journals/finr/finr2017.html#journals/finr/TaniguchiTC17

担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（学術雑誌）

DOI： 10.1109/TCDS.2016.2565542

Kyutacar

arXiv

その他リンク： http://dblp.uni-trier.de/db/journals/tamd/tamd8.html#journals/tamd/TaniguchiTI16

担当区分：筆頭著者   記述言語：日本語   掲載種別：研究論文（その他学術会議資料等）

DOI： 10.5687/iscie.27.166

Kyutacar

CiNii Article

J-GLOBAL

その他リンク： http://jglobal.jst.go.jp/public/201402260823205620

記述言語：日本語   掲載種別：記事・総説・解説・論説等（学術雑誌）

DOI： 10.11509/isciesci.66.4_151

Kyutacar

CiNii Article

その他リンク： https://doi.org/10.11509/isciesci.66.4_151

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

Kyutacar

その他リンク： https://researchmap.jp/ataniguchi/published_papers/55172813

記述言語：英語   掲載種別：研究論文（その他学術会議資料等）

Kyutacar

arXiv

その他リンク： https://arxiv.org/abs/2605.28087

記述言語：英語   掲載種別：研究論文（その他学術会議資料等）

Kyutacar

arXiv

その他リンク： https://arxiv.org/abs/2603.28156

記述言語：英語   掲載種別：研究論文（学術雑誌）

This study explores collaborative music generation using multiple symbolic music generation AI agents, grounding the process in the Systems Model of Creativity. We propose the Metropolis-Hastings Music Generation Game (MH-MuG), which integrates latent diffusion models (LDMs) and formulates the collaboration as a decentralized Bayesian inference process. In MH-MuG, agents with distinct musical knowledge (pre-trained on Classical and Jazz) alternate between composer and listener roles. This interaction functions as a Markov Chain Monte Carlo (MCMC) method, enabling agents to collectively sample from a joint distribution that integrates their knowledge. We compared two variants: one without fine-tuning (w/o f.t.), modeling a fixed-knowledge game, and one with fine-tuning (w/ f.t.), modeling mutual adaptation. Our experiments yielded two key findings: (1) The (w/o f.t.) variant, functioning as a collaborative music generation game, successfully generated high-quality, stylistically fused music. (2) Conversely, the (w/ f.t.) variant led to a significant reduction in diversity. We interpret this not as a failure, but as a computational demonstration of the “siloing” phenomenon that occurs when creative interactions are limited to a closed loop.

DOI： 10.1109/ACCESS.2026.3666234

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Scopus

その他リンク： https://doi.org/10.1109/ACCESS.2026.3666234

担当区分：筆頭著者,　最終著者,　責任著者   記述言語：日本語   掲載種別：記事・総説・解説・論説等（学術雑誌）

<p></p>

DOI： 10.7210/jrsj.44.468

CiNii Research

記述言語：日本語   掲載種別：研究論文（学術雑誌）

DOI： 10.7210/jrsj.43.703

Kyutacar

その他リンク： https://doi.org/10.7210/jrsj.43.703

担当区分：最終著者   記述言語：英語   掲載種別：研究論文（その他学術会議資料等）

Kyutacar

arXiv

その他リンク： https://arxiv.org/abs/2410.21803

記述言語：日本語   掲載種別：記事・総説・解説・論説等（学術雑誌）

DOI： 10.11517/jjsai.39.2_205

Kyutacar

CiNii Research

その他リンク： http://id.ndl.go.jp/bib/033376099

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

Service robots operating in unfamiliar environments require capabilities for autonomous object recognition and learning from user interactions. However, present semantic segmentation methods, crucial for such tasks, often demand large datasets and costly annotations to achieve accurate inference. In addition, they cannot handle all possible objects or environmental variations without a large additional number of images and annotations. Therefore, this study introduces a learning system for semantic segmentation that combines 3D semantic mapping with interactions between an autonomous mobile robot and a user. We show that the proposed system can: 1) autonomously construct 3D semantic maps using an autonomous mobile robot, 2) improve the prediction accuracy of models pre-trained by supervised and weakly supervised learning in new environments, even without interaction, and 3) more accurately predict new classes of objects with a small number of additional coarse annotations obtained through interaction. Results obtained from experiments conducted in a real-world setting using models pre-trained on the NYU, VOC, and COCO datasets demonstrated an improvement in semantic segmentation accuracy when using our proposed system.

DOI： 10.1109/SII58957.2024.10417237

Kyutacar

Scopus

その他リンク： https://doi.org/10.1109/SII58957.2024.10417237

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

This study investigates the emergence of compositionality and generalization within Emergent Communication (EmCom) systems, focusing on emergent language using the Metropolis-Hastings naming game (MHNG). Although the MHNG has been used in previous EmCom research, this is the first study to explore compositionality, the ability to construct complex expressions by combining simpler elements, and generalization, the ability to apply learned patterns to new situations, in emergent language within this language game. We introduce the novel Inter-VAE+VAE model, which equips each agent with dual variational autoencoders (VAEs), specifically designed for cognitive tasks that mirror human perception and language processing. This model, facilitating predictive coding, allows agents to refine their world models through collective experiences, a process rooted in the collective predictive coding hypothesis and differing from isolated learning approaches. Our model was evaluated against baseline models, including the β-VAE and βTCVAE, and was further compared with implementations of the Lewis signaling game using the dSprites and 3Dshapes datasets. The results from these evaluations indicate an improvement in agent communication within the MH naming game and underscore the model's potential in replicating key aspects of human language, particularly compositionality and generalization, in artificial systems.

DOI： 10.1109/ICDL61372.2024.10644635

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85203808795&origin=inward

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

The ability to understand a variety of verbal instructions and perform tasks is important for daily life support robots. People's speech to the robot may include greetings and demonstratives, and the robot needs to act appropriately in such cases. In the case of instruction including demonstratives such as 'Bring me that cup,' exophora resolution (ER) is needed to find the specific object corresponding to 'that cup.' Recently, action planning based on large language models (LLMs) has been necessary for robots to act and has been rapidly studied. However, in previous action planning research, it was difficult to identify the target object from language instructions including demonstratives, and to perform the planning desired by the user. This study aims to plan actions expected by the user from various user language queries, such as greetings or those containing demonstratives. We propose a method for action planning from a variety of language instructions by combining a task classification module, an ER framework, and LLMs. In the experiment, various queries were given, including queries containing demonstratives and queries that do not require the robot to move but only to respond, and the planning accuracy was compared to a baseline without an exophora resolution framework. The results show that the proposed method is approximately 1.3 times more accurate than the baseline. The project website is https://emergentsystemlabstudent.github.io/ECRAP/.

DOI： 10.1109/IRC63610.2024.00007

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85216923232&origin=inward

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

Humans create shared symbols through interactions with others and the environment, enabling effective communication within a specific community. This process, known as emergent communication, facilitates the exchange of intentions and meanings. Furthermore, humans combine phonemes, the smallest units of sounds, to create a myriad of continuous symbols, a property called compositionality. The generation and recognition capabilities of these speech signals are acquired through interactions with others. Although existing studies have promoted the emergence of discrete symbols representing observed objects through two agents, the emergent creation of continuous signals with compositionality is yet to be studied. This study proposes a novel probabilistic generative model that integrates the Metropolis-Hastings naming game (MHNG) and Gaussian process hidden semi-Markov model (GP-HSMM), allowing the emergence of continuous symbols. MHNG allows symbol emergence through communication between independent individuals without directly observing the internal states of others. Moreover, GP-HSMM supports the unsupervised segmentation of continuous signals and acquires continuous signals with compositionality as shared symbols. We conducted experiments using the upper and lower portions of MNIST as the observed images to evaluate the efficacy of our proposed approach. The results demonstrate that the agents successfully generate and recognize shared continuous signals representing MNIST digits despite possessing different internal representations.

DOI： 10.1109/ICDL61372.2024.10644981

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85203807918&origin=inward

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

This study proposes a novel probabilistic generative model based on the Gaussian process that enables symbols to emerge from two agents. In this model, agents can create symbols and share their meaning with other agents in a bottom-up manner by interacting with each other and with the environment. The symbols are formulated as shared continuous latent variables from which two agents' observations are generated according to the Gaussian process; therefore, symbol emergence is considered equivalent to an inference of the latent variables. For the inference, we used the Metropolis-Hastings (MH) naming game, in which shared latent variables can be inferred without directly observing another variable's internal state. Hence, symbols can emerge while maintaining agent independence. In the experiment, we used a multimodal object dataset, and examined the emergence of symbols representing the objects. To confirm that the symbols and meanings were appropriately shared among the agents, we evaluated whether agents could select an object represented by a symbol yielded by another agent. The results show that the agent could select an appropriate object using the symbols that emerged.

DOI： 10.1109/ICDL61372.2024.10644473

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85203795628&origin=inward

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

Daily life support robots in the home environment interpret the user's pointing and understand the instructions, thereby increasing the number of instructions accomplished. This study aims to improve the estimation performance of pointing frames by using speech information when a person gives pointing or verbal instructions to the robot. The estimation of the pointing frame, which represents the moment when the user points, can help the user understand the instructions. Therefore, we perform pointing frame estimation using a time-series model, utilizing the user's speech, images, and speech-recognized text observed by the robot. In our experiments, we set up realistic communication conditions, such as speech containing everyday conversation, non-upright posture, actions other than pointing, and reference objects outside the robot's field of view. The results showed that adding speech information improved the estimation performance, especially the Transformer model with Mel-Spectrogram as a feature. This study will lead to be applied to object localization and action planning in 3D environments by robots in the future. The project website is https://emergentsystemlabstudent.github.io/PointingImgEst/.

DOI： 10.1109/SMC54092.2024.10831649

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85216926831&origin=inward

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

Improving instance-specific image goal navigation (InstanceImageNav), which involves locating an object in the real world that is identical to a query image, is essential for enabling robots to help users find desired objects. The challenge lies in the domain gap between the low-quality images observed by the moving robot, characterized by motion blur and low resolution, and the high-quality query images provided by the user. These domain gaps can significantly reduce the task success rate, yet previous work has not adequately addressed them. To tackle this issue, we propose a novel method: few-shot cross-quality instance-aware adaptation (CrossIA). This approach employs contrastive learning with an instance classifier to align features between a large set of low-quality images and a small set of high-quality images. We fine-tuned the SimSiam model, pretrained on ImageNet, using CrossIA with instance labels based on a 3D semantic map. Additionally, our system integrates object image collection with a pretrained deblurring model to enhance the quality of the observed images. Evaluated on an InstanceImageNav task with 20 different instance types, our method improved the task success rate by up to three-fold compared to a baseline based on SuperGlue. These findings highlight the potential of contrastive learning and image enhancement techniques in improving object localization in robotic applications. The project website is https://emergentsystemlabstudent.github.io/DomainBridgingNav/.

DOI： 10.1109/IRC63610.2024.00032

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85216933039&origin=inward

担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

Kyutacar

その他リンク： https://researchmap.jp/ataniguchi/published_papers/47339884

担当区分：最終著者   記述言語：英語   掲載種別：研究論文（学術雑誌）

We explore the emergence of symbols during interactions between individuals through an experimental semiotic study. Previous studies have investigated how humans organize symbol systems through communication using artificially designed subjective experiments. In this study, we focused on a joint-attention-naming game (JA-NG) in which participants independently categorized objects and assigned names while assuming their joint attention. In the Metropolis-Hastings naming game (MHNG) theory, listeners accept provided names according to the acceptance probability computed using the Metropolis-Hastings (MH) algorithm. The MHNG theory suggests that symbols emerge as an approximate decentralized Bayesian inference of signs, which is represented as a shared prior variable if the conditions of the MHNG are satisfied. This study examines whether human participants exhibit behavior consistent with the MHNG theory when playing the JA-NG. 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. The main contributions of this study are twofold. First, we reject the null hypothesis that humans make acceptance judgments with a constant probability, regardless of the acceptance probability calculated by the MH algorithm. The results of this study show that the model with acceptance probability computed by the MH algorithm predicts human behavior significantly better than the model with a constant probability of acceptance. Second, the MH-based model predicted human acceptance/rejection behavior more accurately than four other models (i.e., Constant, Numerator, Subtraction, Binary). Among the models compared, the model using the MH algorithm, which is the only model with the mathematical support of decentralized Bayesian inference, predicted human behavior most accurately, suggesting that symbol emergence in the JA-NG can be explained by the MHNG.

DOI： 10.3389/frai.2023.1235231

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Scopus

その他リンク： https://doi.org/10.3389/frai.2023.1235231

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

This paper proposes an integrated model of multi-agent reinforcement learning with emergent communication based on probabilistic generative models called MASAC- ECo that enables two agents to learn cooperative actions. In this model, agents receive messages as discrete symbols to communicate the state of each agent based on the Metropolis-Hastings naming game (MHNG). Using MHNG, the messages can emerge with-out directly observing another agent's state, and the emerging messages enable each agent to know the other agent's state indirectly. Furthermore, each agent policy is learned using a soft actor-critic, and by utilizing the emerging message as an input of the soft actor-critic, each agent can learn cooperative actions depending on its state and message. In the experiment, we demonstrated that MASAC- ECo can learn cooperative actions. Moreover, the experimental results show that its performance is comparable with that of the conventional method, even though in the training phase, the latter can use the other's state directly, whereas MASAC-ECo cannot.

DOI： 10.1109/IIAI-AAI-Winter61682.2023.00073

Kyutacar

Scopus

その他リンク： https://doi.org/10.1109/IIAI-AAI-Winter61682.2023.00073

記述言語：英語   掲載種別：研究論文（学術雑誌）

Our study introduces a novel approach that combined probabilistic logic and multimodal spatial concepts to enable a robot to efficiently acquire place–object relationships in a new home environment with few learning iterations. By leveraging probabilistic logic, which employs predicate logic with probability values, we represent common-sense knowledge of the place–object relationships. The integration of logical inference and cross-modal inference to calculate conditional probabilities across different modalities enables the robot to infer object locations even when their likely locations are undefined. To evaluate the effectiveness of our method, we conducted simulation experiments and compared the results with three baselines: multimodal spatial concepts only, common-sense knowledge only, and common-sense knowledge and multimodal spatial concepts combined. By comparing the number of room visits required by the robot to locate 24 objects, we demonstrated the improved performance of our approach. For search tasks including objects whose locations were undefined, the findings demonstrate that our method reduced the learning cost by a factor of 1.6 compared to the baseline methods. Additionally, we conducted a qualitative analysis in a real-world environment to examine the impact of integrating the two inferences and identified the scenarios that influence changes in the task success rate.

DOI： 10.1080/18824889.2023.2283954

Kyutacar

Scopus

その他リンク： https://doi.org/10.1080/18824889.2023.2283954

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

Cross-situational word learning (CSL) is a fast and efficient method for humans to acquire word meanings. Many studies have replicated human CSL using computational models. Among these, cross-situational learning with Bayesian probabilistic generative model (CSL-PGM) can estimate word meanings from observations that include multiple attributes, such as color and shape. However, as CSL-PGM receives observations for each attribute on a separate channel, it cannot perform CSL for images with multiple attributes. Therefore, we introduce a disentangled representation that captures the attributes within an image. Additionally, we propose CSL+VAE, which integrates CSL-PGM and a β-VAE to obtain a disentangled representation in an unsupervised manner. CSL+VAE can discover attributes hidden in images and word sequences and infer the meanings of words. Additionally, it can obtain a more disentangled representation using a learning framework wherein both models share parameters. During experiments, the model was trained on a set of images comprising five attributes and one to five words describing them. The results showed that 99.9% of the words correctly estimated the attributes of the words and correctly estimated the correspondence between the image features and the words. The proposed model also outperformed existing multimodal models in inferring images from word sequences, achieving an accuracy of 0.870.

DOI： 10.1109/ICDL55364.2023.10364474

Kyutacar

Scopus

その他リンク： https://doi.org/10.1109/ICDL55364.2023.10364474

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

Human infants acquire lexicons by inferring word meanings through cross-situational learning (CSL) and word segmentation. This study models robot lexical acquisition by segmenting sentences using sensory information as clues and inferring word meanings through the co-occurrence of words and sensory information using CSL. This process is called the mutual segmentation of sentences and multi-attribute sensory information. There are two challenges in modeling lexical acquisition based on mutual segmentation: (i) Segmenting sentences and learning word meaning simultaneously for mutual segmentation; (ii) Consideration of words that are not associated with sensory information. The proposed method extends the probabilistic generative model of CSL to infer whether words are related to sensory information. In addition, the proposed method mathematically integrates an unsupervised word segmentation method. In the experiments, an agent acquired lexicons using multi-attribute sensory information collected in simulated environments and linguistic information. The results demonstrate that mutual segmentation leads to a 2.1% improvement in F1-score for word segmentation performance, and reduces the influence of word segmentation errors on the estimation of word attributes and meanings. Furthermore, the results indicate that considering ungrounded words improves the performance of sensory information clustering and word segmentation.

DOI： 10.1109/ICDL55364.2023.10364383

Kyutacar

Scopus

その他リンク： https://doi.org/10.1109/ICDL55364.2023.10364383

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

In this study, we present a novel approach to lexical acquisition using a mixture-of-product-of-experts (MoPoE) multimodal recurrent state-space model (MRSSM) with an assessment of its potential. The proposed MoPoE-MRSSM can aggregate multimodal information and perform cross-modal inference. Our approach is designed to provide a model of the process of lexical knowledge acquisition performed by infants in terms of word units, correspondences between words and objects, and transition probabilities between words by analyzing sequences of multimodal input streams comprising visual and auditory information. The proposed MoPoE-MRSSM was evaluated using a simple multimodal dataset that combined visual and speech information representing numbers from the MNIST dataset and TIDIGITS corpus. The results of an experimental evaluation of our method suggest that MoPoE-MRSSM can learn word units, i.e., the phoneme transition rules within words, and that the use of multimodal input improves its prediction performance. Because our method was not able to predict the probability of transitions between words sufficiently for practical applications, we also suggest some promising avenues for further research to explore this approach in more detail.

DOI： 10.1109/ICDL55364.2023.10364429

Kyutacar

Scopus

その他リンク： https://doi.org/10.1109/ICDL55364.2023.10364429

記述言語：英語   掲載種別：研究論文（学術雑誌）

In the studies on symbol emergence and emergent communication in a population of agents, a computational model was employed in which agents participate in various language games. Among these, the Metropolis-Hastings naming game (MHNG) possesses a notable mathematical property: symbol emergence through MHNG is proven to be a decentralized Bayesian inference of representations shared by the agents. However, the previously proposed MHNG is limited to a two-agent scenario. This paper extends MHNG to an N-agent scenario. 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, i.e., YCB object dataset, enabling multiple agents to develop and share a symbol system. Furthermore, we introduce two types of approximations—one-sample and limited-length—to reduce computational complexity while maintaining the ability to explain communication in a population of agents. The experimental findings showcased the efficacy of RMHNG as a decentralized Bayesian inference for approximating the posterior distribution concerning latent variables, which are jointly shared among agents, akin to MHNG, although the improvement in ARI and κ coefficient is smaller in the real image dataset condition. Moreover, the utilization of RMHNG elucidated the agents' capacity to exchange symbols. Furthermore, the study discovered that even the computationally simplified version of RMHNG could enable symbols to emerge among the agents.

DOI： 10.3389/frai.2023.1229127

Kyutacar

Scopus

その他リンク： https://doi.org/10.3389/frai.2023.1229127

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

To enable a robot to provide support in a home environment through human-robot interaction, exophora resolution is crucial for accurately identifying the target of ambiguous linguistic instructions, which may include a demonstrative, such as 'Take that one'. Unlike endophora resolution, which involves predicting the corresponding word from given sentences, exophora resolution necessitates comprehensive utilization of external real-world information to identify and disambiguate the target from the on-site environment. This study aims to resolve ambiguity in language instructions containing a demonstrative through exophora resolution, utilizing real-world multimodal information. The robot accomplishes this by using three types of information: 1) object categories, 2) demonstratives, and 3) pointing, as well as knowledge about objects obtained from the robot's pre-exploration of the environment. We evaluated the accuracy of object identification under multiple conditions by identifying a user-indicated object in a field that mimics a home environment. Our results demonstrate that our proposed method of exophora resolution using multimodal information can identify the target with two to three times higher accuracy than baseline methods in cases where information is missing.

DOI： 10.1109/RO-MAN57019.2023.10309487

Kyutacar

Scopus

その他リンク： https://doi.org/10.1109/RO-MAN57019.2023.10309487

記述言語：英語   掲載種別：研究論文（学術雑誌）

Kyutacar

その他リンク： https://researchmap.jp/ataniguchi/published_papers/47271934

記述言語：英語   掲載種別：研究論文（学術雑誌）

Kyutacar

その他リンク： https://researchmap.jp/ataniguchi/published_papers/47271932

担当区分：筆頭著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）

Autonomous robots need to learn the categories of various places by exploring their environments and interacting with users. However, preparing training datasets with linguistic instructions from users is time-consuming and labor-intensive. Moreover, effective exploration is essential for appropriate concept formation and rapid environmental coverage. To address this issue, we propose an active inference method, referred to as spatial concept formation with information gain-based active exploration (SpCoAE) that combines sequential Bayesian inference using particle filters and information gain-based destination determination in a probabilistic generative model. This study interprets the robot's action as a selection of destinations to ask the user, ‘What kind of place is this?’ in the context of active inference. 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. Our experiment demonstrated the effectiveness of the SpCoAE in efficiently determining a destination for learning appropriate spatial concepts in home environments.

DOI： 10.1080/01691864.2023.2225175

Kyutacar

Scopus

CiNii Research

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85162863778&origin=inward

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

We propose a novel method that integrates probabilistic logic and multimodal spatial concepts to enable a robot to acquire the relationships between places and objects in a new environment with a few learning times. Using predicate logic with probability values (i.e., probabilistic logic) to represent commonsense knowledge of place-object relationships, we combine logical inference using probabilistic logic with the cross-modal inference that can calculate the conditional probabilities of other modalities given one modality. This allows the robot to infer the place of the object to find even when it does not know the likely place of the object in the home environment. We conducted experiments in which a robot searched for daily objects, including objects with undefined places, in a simulated home environment using four approaches: 1) multimodal spatial concepts only, 2) commonsense knowledge only, 3) commonsense knowledge and multimodal spatial concepts, and 4) probabilistic logic and multimodal spatial concepts (proposed). We confirmed the effectiveness of the proposed method by comparing the number of place visits it took for the robot to find all the objects. We also observed that our proposed approach reduces the on-site learning cost by a factor of 1.6 over the three baseline methods when the robot performs the task of finding objects with undefined places in a new home environment.

DOI： 10.1109/SII55687.2023.10039318

Kyutacar

Scopus

その他リンク： https://doi.org/10.1109/SII55687.2023.10039318

記述言語：英語   掲載種別：記事・総説・解説・論説等（学術雑誌）

DOI： 10.3389/fnbot.2022.1077891

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85143164401&origin=inward

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

Enabling robots to learn from interactions with users is essential to perform service tasks. However, as a robot categorizes objects from multimodal information obtained by its sensors during interactive onsite teaching, the inferred names of unknown objects do not always match the human user's expectation, especially when the robot is introduced to new environments. Confirming the learning results through natural speech interaction with the robot often puts an additional burden on the user who can only listen to the robot to validate the results. Therefore, we propose a human-robot interface to reduce the burden on the user by visualizing the inferred results in mixed reality (MR). In particular, we evaluate the proposed interface on the system usability scale (SUS) and the NASA task load index (NASA-TLX) with three experimental object categorization scenarios based on multimodal latent Dirichlet allocation (MLDA) in which the robot: 1) does not share the inferred results with the user at all, 2) shares the inferred results through speech interaction with the user (baseline), and 3) shares the inferred results with the user through an MR interface (proposed). We show that providing feedback through an MR interface significantly reduces the temporal, physical, and mental burden on the human user compared to speech interaction with the robot.

DOI： 10.1109/IROS47612.2022.9981374

Kyutacar

Scopus

その他リンク： https://doi.org/10.1109/IROS47612.2022.9981374

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

In this study, we propose a head-to-head type (H2H-type) inter-personal multimodal Dirichlet mixture (Inter-MDM) by modifying the original Inter-MDM, which is a probabilistic generative model that represents the symbol emergence between two agents as multiagent multimodal categorization. A Metropolis-Hastings method-based naming game based on the Inter-MDM enables two agents to collaboratively perform multimodal categorization and share signs with a solid mathematical foundation of convergence. However, the conventional Inter-MDM presumes a tail-to-tail connection across a latent word variable, causing inflexibility of the further extension of Inter-MDM for modeling a more complex symbol emergence. Therefore, we propose herein a head-to-head type (H2H-type) Inter-MDM that treats a latent word variable as a child node of an internal variable of each agent in the same way as many prior studies of multimodal categorization. On the basis of the H2H-type Inter-MDM, we propose a naming game in the same way as the conventional Inter-MDM. The experimental results show that the H2H-type Inter-MDM yields almost the same performance as the conventional Inter-MDM from the viewpoint of multimodal categorization and sign sharing.

DOI： 10.1109/ICDL53763.2022.9962227

Kyutacar

Scopus

その他リンク： https://doi.org/10.1109/ICDL53763.2022.9962227

記述言語：英語   掲載種別：研究論文（学術雑誌）

<jats:p>Robots employed in homes and offices need to adaptively learn spatial concepts using user utterances. To learn and represent spatial concepts, the robot must estimate the coordinate system used by humans. For example, to represent spatial concept “left,” which is one of the <jats:italic>relative spatial concepts</jats:italic> (defined as a spatial concept depending on the object’s location), humans use a coordinate system based on the direction of a reference object. As another example, to represent spatial concept “living room,” which is one of the <jats:italic>absolute spatial concepts</jats:italic> (defined as a spatial concept that does not depend on the object’s location), humans use a coordinate system where a point on a map constitutes the origin. Because humans use these concepts in daily life, it is important for the robot to understand the spatial concepts in different coordinate systems. However, it is difficult for robots to learn these spatial concepts because humans do not clarify the coordinate system. Therefore, we propose a method (RASCAM) that enables a robot to simultaneously estimate the coordinate system and spatial concept. The proposed method is based on ReSCAM+O, which is a learning method for relative spatial concepts based on a probabilistic model. The proposed method introduces a latent variable that represents a coordinate system for simultaneous learning. This method can simultaneously estimate three types of unspecified information: coordinate systems, reference objects, and the relationship between concepts and words. No other method can estimate all these three types. Experiments using three different coordinate systems demonstrate that the proposed method can learn both relative and absolute spatial concepts while accurately selecting the coordinate system. The proposed approach can be beneficial for service robots to flexibly understand a new environment through the interactions with humans.</jats:p>

DOI： 10.3389/frobt.2022.904751

Kyutacar

Scopus

CiNii Research

PubMed

その他リンク： https://doi.org/10.3389/frobt.2022.904751

記述言語：英語   掲載種別：研究論文（学術雑誌）

Building a human-like integrative artificial cognitive system, that is, an artificial general intelligence (AGI), is the holy grail of the artificial intelligence (AI) field. Furthermore, a computational model that enables an artificial system to achieve cognitive development will be an excellent reference for brain and cognitive science. This paper describes an approach to develop a cognitive architecture by integrating elemental cognitive modules to enable the training of the modules as a whole. 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 architecture to develop a cognitive system for developmental robots by integrating PGMs. The proposed development framework is called a whole brain PGM (WB-PGM), which differs fundamentally from existing cognitive architectures in that it can learn continuously through a system based on sensory-motor information.

In this paper, we describe the rationale for WB-PGM, the current status of PGM-based elemental cognitive modules, their relationship with the human brain, the approach to the integration of the cognitive modules, and future challenges. Our findings can serve as a reference for brain studies. As PGMs describe explicit informational relationships between variables, WB-PGM provides interpretable guidance from computational sciences to brain science. By providing such information, researchers in neuroscience can provide feedback to researchers in AI and robotics on what the current models lack with reference to the brain. Further, it can facilitate collaboration among researchers in neuro-cognitive sciences as well as AI and robotics.

DOI： 10.1016/j.neunet.2022.02.026

Scopus

CiNii Research

PubMed

その他リンク： https://oist.repo.nii.ac.jp/records/2673

担当区分：筆頭著者,　責任著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

The human brain, among its several functions, analyzes the double articulation structure in spoken language, i.e., double articulation analysis (DAA). A hierarchical structure in which words are connected to form a sentence and words are composed of phonemes or syllables is called a double articulation structure. Where and how DAA is performed in the human brain has not been established, although some insights have been obtained. In addition, existing computational models based on a probabilistic generative model (PGM) do not incorporate neuroscientific findings, and their consistency with the brain has not been previously discussed. This study compared, mapped, and integrated these existing computational models with neuroscientific findings to bridge this gap, and the findings are relevant for future applications and further research. This study proposes a PGM for a DAA hypothesis that can be realized in the brain based on the outcomes of several neuroscientific surveys. The study involved (i) investigation and organization of anatomical structures related to spoken language processing, and (ii) design of a PGM that matches the anatomy and functions of the region of interest. Therefore, this study provides novel insights that will be foundational to further exploring DAA in the brain.

DOI： 10.1109/ICDL53763.2022.9962216

Kyutacar

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85143991066&origin=inward

記述言語：英語   掲載種別：研究論文（学術雑誌）

This paper proposes methods for unsupervised lexical acquisition for relative spatial concepts using spoken user utterances. A robot with a flexible spoken dialog system must be able to acquire linguistic representation and its meaning specific to an environment through interactions with humans as children do. Specifically, relative spatial concepts (e.g., front and right) are widely used in our daily lives, however, it is not obvious which object is a reference object when a robot learns relative spatial concepts. Therefore, we propose methods by which a robot without prior knowledge of words can learn relative spatial concepts. The methods are formulated using a probabilistic model to estimate the proper reference objects and distributions representing concepts simultaneously. 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. Additionally, we show that two processes in the proposed method improve the estimation accuracy of the concepts: generating candidate word sequences by class n-gram and selecting word sequences using location information. Furthermore, we show that clues to reference objects improve accuracy even though the number of candidate reference objects increases.

DOI： 10.1080/01691864.2021.2007168

Kyutacar

Scopus

CiNii Research

その他リンク： https://doi.org/10.1080/01691864.2021.2007168

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

Kyutacar

その他リンク： https://sites.google.com/site/ataniguchi0403/publications

担当区分：筆頭著者,　責任著者   記述言語：英語   掲載種別：研究論文（学術雑誌）

Tidy-up tasks by service robots in home environments are challenging in robotics applications because they involve various interactions with the environment. In particular, robots are required not only to grasp, move, and release various home objects but also to plan the order and positions for placing the objects. In this paper, we propose a novel planning method that can efficiently estimate the order and positions of the objects to be tidied up by learning the parameters of a probabilistic generative model. The model allows a robot to learn the distributions of the co-occurrence probability of the objects and places to tidy up using the multimodal sensor information collected in a tidied environment. Additionally, we develop an autonomous robotic system to perform the tidy-up operation. 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. The simulation results show that the proposed method enables the robot to successively tidy up several objects and achieves the best task score among the considered baseline tidy-up methods.

DOI： 10.1080/01691864.2021.1890212

Kyutacar

Scopus

CiNii Research

その他リンク： https://www.tandfonline.com/doi/pdf/10.1080/01691864.2021.1890212

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

Preserving the linguistic content of input speech is essential during voice conversion (VC). The star generative adversarial network-based VC method (StarGAN-VC) is a recently developed method that allows non-parallel many-to-many VC. 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. To overcome this problem, we propose the use of automatic speech recognition to assist model training, to improve StarGAN-VC, especially in low-resource scenarios. Experimental results show that using our proposed method, StarGAN-VC can retain more linguistic information than vanilla StarGAN-VC.

DOI： 10.21437/Interspeech.2021-492

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85119250014&origin=inward

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

In semantic mapping, which connects semantic information to an environment map, it is a challenging task for robots to deal with both local and global information of environments. In addition, it is important to estimate semantic information of unobserved areas from already acquired partial observations in a newly visited environment. On the other hand, previous studies on spatial concept formation enabled a robot to relate multiple words to places from bottom-up observations even when the vocabulary was not provided beforehand. However, the robot could not transfer global information related to the room arrangement between semantic maps from other environments. In this paper, we propose SpCoMapGAN, which generates the semantic map in a newly visited environment by training an inference model using previously estimated semantic maps. SpCoMapGAN uses generative adversarial networks (GANs) to transfer semantic information based on room arrangements to a newly visited environment. Our proposed method assigns semantics to the map of an unknown environment using the prior distribution of the map trained in known environments and the multimodal observations made in the unknown environment. We experimentally show in simulation that SpCoMapGAN can use global information for estimating the semantic map and is superior to previous methods. Finally, we also demonstrate in a real environment that SpCoMapGAN can accurately 1) deal with local information, and 2) acquire the semantic information of real places.

DOI： 10.1109/IROS45743.2020.9341456

Scopus

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85102410694&origin=inward

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

Kyutacar

その他リンク： https://sites.google.com/site/ataniguchi0403/publications

記述言語：英語   掲載種別：研究論文（学術雑誌）

DOI： 10.3389/frobt.2019.00031

Kyutacar

その他リンク： https://doi.org/10.3389/frobt.2019.00031

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

Kyutacar

その他リンク： https://sites.google.com/site/ataniguchi0403/publications

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

DOI： 10.1007/978-3-319-70022-9_12

Kyutacar

その他リンク： https://doi.org/10.1007/978-3-319-70022-9_12

担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

Kyutacar

その他リンク： https://sites.google.com/site/ataniguchi0403/publications

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

DOI： 10.1109/DEVLRN.2017.8329833

Kyutacar

その他リンク： https://doi.org/10.1109/DEVLRN.2017.8329833

担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

Kyutacar

その他リンク： https://sites.google.com/site/ataniguchi0403/publications

担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

DOI： 10.1016/j.ifacol.2016.10.528

Kyutacar

その他リンク： https://doi.org/10.1016/j.ifacol.2016.10.528

担当区分：筆頭著者   記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

DOI： 10.1007/978-3-319-48036-7_5

Kyutacar

その他リンク： https://doi.org/10.1007/978-3-319-48036-7_5

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）

DOI： 10.1109/IECON.2015.7392291

Kyutacar

その他リンク： https://doi.org/10.1109/IECON.2015.7392291

開催期間： 2024年09月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/48263427

開催期間： 2024年09月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/48263425

開催期間： 2024年09月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/48263422

開催期間： 2024年03月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/47340184

開催期間： 2024年01月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/47340188

開催期間： 2022年03月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/36727374

開催期間： 2021年11月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/36727375

開催期間： 2021年09月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/36727378

開催期間： 2021年09月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/36727377

開催期間： 2021年09月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/36727376

開催期間： 2021年06月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/36727382

開催期間： 2021年06月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/36727381

開催期間： 2021年06月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/36727380

開催期間： 2021年06月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/36727379

開催期間： 2020年10月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/36727385

開催期間： 2020年10月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/36727384

開催期間： 2020年10月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/36727383

開催期間： 2020年06月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/36727388

開催期間： 2020年06月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/36727387

開催期間： 2020年06月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/36727386

開催期間： 2019年06月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/12721355

開催期間： 2019年06月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/12721354

開催期間： 2018年11月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/12721349

開催期間： 2018年11月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/12721344

開催期間： 2018年09月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/21337884

開催期間： 2018年09月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/21337881

開催期間： 2018年07月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/21033321

開催期間： 2017年06月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/21033317

開催期間： 2016年12月06日   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/21032677

開催期間： 2016年12月06日   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/21032675

開催期間： 2016年09月07日   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/21032680

開催期間： 2016年09月07日   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/21032679

開催期間： 2015年11月18日   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/21032690

開催期間： 2015年11月18日   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/21032689

開催期間： 2015年11月18日   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/21032688

開催期間： 2015年09月03日   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/21032691

開催期間： 2015年05月20日   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/21032692

開催期間： 2014年11月21日   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/21032696

開催期間： 2014年09月04日   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/21032697

開催期間： 2013年11月18日   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/21032701

開催期間： 2013年05月15日   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/21032703

開催期間： 2026年10月03日   記述言語：英語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/55172849

開催期間： 2026年10月03日   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/55172856

開催期間： 2026年06月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/54896067

開催期間： 2026年06月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/54896070

開催期間： 2026年06月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/54896068

開催期間： 2025年12月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/54896071

開催期間： 2025年05月   記述言語：日本語  

その他リンク： https://sites.google.com/site/ataniguchi0403/publications

開催期間： 2025年05月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/54896072

開催期間： 2025年05月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/54896075

開催期間： 2025年05月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/54896074

開催期間： 2025年05月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/54896073

開催期間： 2025年03月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/54896078

開催期間： 2025年03月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/54896079

開催期間： 2024年12月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/54896076

開催期間： 2024年12月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/54896077

開催期間： 2024年09月   記述言語：日本語  

その他リンク： https://sites.google.com/site/ataniguchi0403/publications

開催期間： 2024年05月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/47559076

開催期間： 2024年05月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/47559080

開催期間： 2024年05月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/47559078

開催期間： 2024年03月   記述言語：日本語  

その他リンク： https://sites.google.com/site/ataniguchi0403/publications

開催期間： 2024年03月   記述言語：日本語  

その他リンク： https://sites.google.com/site/ataniguchi0403/publications

開催期間： 2024年03月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/46782335

開催期間： 2023年10月   記述言語：日本語  

その他リンク： https://sites.google.com/site/ataniguchi0403/publications

開催期間： 2023年09月   記述言語：日本語  

その他リンク： https://sites.google.com/site/ataniguchi0403/publications

開催期間： 2023年09月   記述言語：日本語  

その他リンク： https://sites.google.com/site/ataniguchi0403/publications

開催期間： 2023年09月   記述言語：日本語  

その他リンク： https://sites.google.com/site/ataniguchi0403/publications

開催期間： 2023年09月   記述言語：日本語  

その他リンク： https://sites.google.com/site/ataniguchi0403/publications

開催期間： 2023年09月   記述言語：日本語  

その他リンク： https://sites.google.com/site/ataniguchi0403/publications

開催期間： 2023年06月   記述言語：日本語  

その他リンク： https://sites.google.com/site/ataniguchi0403/publications

開催期間： 2023年06月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/43324109

開催期間： 2023年06月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/43447440

開催期間： 2023年06月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/43447438

開催期間： 2023年06月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/43447436

開催期間： 2023年06月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/43447434

開催期間： 2023年06月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/43447432

開催期間： 2023年06月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/43324122

開催期間： 2023年03月   記述言語：日本語  

その他リンク： https://sites.google.com/site/ataniguchi0403/publications

開催期間： 2023年03月   記述言語：日本語  

その他リンク： https://sites.google.com/site/ataniguchi0403/publications

開催期間： 2022年09月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/41096425

開催期間： 2022年06月   記述言語：日本語  

その他リンク： https://sites.google.com/site/ataniguchi0403/publications

開催期間： 2022年06月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/41045629

開催期間： 2022年06月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/41045633

開催期間： 2022年06月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/41045632

開催期間： 2022年06月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/41045631

開催期間： 2022年06月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/41045630

開催期間： 2022年03月   記述言語：日本語  

その他リンク： https://sites.google.com/site/ataniguchi0403/publications

開催期間： 2021年12月   記述言語：日本語  

その他リンク： https://sites.google.com/site/ataniguchi0403/publications

開催期間： 2021年12月   記述言語：日本語  

その他リンク： https://sites.google.com/site/ataniguchi0403/publications

開催期間： 2018年06月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/21032667

開催期間： 2018年06月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/21032668

開催期間： 2017年05月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/21032673

開催期間： 2017年05月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/21032674

開催期間： 2016年06月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/21032683

開催期間： 2016年06月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/21032686

開催期間： 2016年06月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/21032685

開催期間： 2015年08月   記述言語：日本語  

その他リンク： https://sites.google.com/site/ataniguchi0403/publications

開催期間： 2015年05月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/21032693

開催期間： 2015年05月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/21032694

開催期間： 2014年05月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/21032700

開催期間： 2013年06月   記述言語：日本語  

その他リンク： https://researchmap.jp/ataniguchi/presentations/21032704

受賞国：日本国

受賞国：日本国

受賞国：日本国

受賞国：日本国

受賞国：アメリカ合衆国

受賞国：日本国

受賞国：日本国

受賞国：日本国

受賞国：日本国

受賞国：日本国

受賞国：日本国

受賞国：日本国

受賞国：日本国

受賞国：日本国

受賞国：日本国

科目区分：大学院専門科目  国名：日本国

科目区分：学部専門科目  国名：日本国

科目区分：学部専門科目  国名：日本国