151 results on '"Taniguchi, Tadahiro"'
Search Results
2. Collective predictive coding hypothesis: symbol emergence as decentralized Bayesian inference.
- Author
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Taniguchi, Tadahiro and Hieida, Chie
- Subjects
LANGUAGE models ,PROBABILISTIC generative models ,SENSE organs ,MULTIAGENT systems ,BAYESIAN field theory - Abstract
Understanding the emergence of symbol systems, especially language, requires the construction of a computational model that reproduces both the developmental learning process in everyday life and the evolutionary dynamics of symbol emergence throughout history. This study introduces the collective predictive coding (CPC) hypothesis, which emphasizes and models the interdependence between forming internal representations through physical interactions with the environment and sharing and utilizing meanings through social semiotic interactions within a symbol emergence system. The total system dynamics is theorized from the perspective of predictive coding. The hypothesis draws inspiration from computational studies grounded in probabilistic generative models and language games, including the Metropolis-Hastings naming game. Thus, playing such games among agents in a distributed manner can be interpreted as a decentralized Bayesian inference of representations shared by a multi-agent system. Moreover, this study explores the potential link between the CPC hypothesis and the free-energy principle, positing that symbol emergence adheres to the society-wide free-energy principle. Furthermore, this paper provides a new explanation for why large language models appear to possess knowledge about the world based on experience, even though they have neither sensory organs nor bodies. This paper reviews past approaches to symbol emergence systems, offers a comprehensive survey of related prior studies, and presents a discussion on CPC-based generalizations. Future challenges and potential cross-disciplinary research avenues are highlighted. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
3. World model learning and inference
- Author
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Friston, Karl, Moran, Rosalyn J., Nagai, Yukie, Taniguchi, Tadahiro, Gomi, Hiroaki, and Tenenbaum, Josh
- Published
- 2021
- Full Text
- View/download PDF
4. Improved and scalable online learning of spatial concepts and language models with mapping
- Author
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Taniguchi, Akira, Hagiwara, Yoshinobu, Taniguchi, Tadahiro, and Inamura, Tetsunari
- Published
- 2020
- Full Text
- View/download PDF
5. Neuro-SERKET: Development of Integrative Cognitive System Through the Composition of Deep Probabilistic Generative Models
- Author
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Taniguchi, Tadahiro, Nakamura, Tomoaki, Suzuki, Masahiro, Kuniyasu, Ryo, Hayashi, Kaede, Taniguchi, Akira, Horii, Takato, and Nagai, Takayuki
- Published
- 2020
- Full Text
- View/download PDF
6. Emergent communication of multimodal deep generative models based on Metropolis-Hastings naming game.
- Author
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Hoang, Nguyen Le, Taniguchi, Tadahiro, Hagiwara, Yoshinobu, Taniguchi, Akira, Filos, Angelos, and Dessi, Roberto
- Subjects
DEEP learning ,ARTIFICIAL neural networks ,NATURAL language processing ,INTERPERSONAL communication ,PREHENSION (Physiology) ,PATTERN recognition systems ,TELECOMMUNICATION systems ,DEEP brain stimulation - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
7. Emergent communication through Metropolis-Hastings naming game with deep generative models.
- Author
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Taniguchi, Tadahiro, Yoshida, Yuto, Matsui, Yuta, Le Hoang, Nguyen, Taniguchi, Akira, and Hagiwara, Yoshinobu
- Subjects
- *
DEEP reinforcement learning , *REINFORCEMENT learning , *PROBABILISTIC generative models , *REWARD (Psychology) , *BAYESIAN field theory , *COMPUTATIONAL neuroscience , *NEUROLINGUISTICS - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
8. Dream to posture: visual posturing of a tendon-driven hand using world model and muscle synergies.
- Author
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Ishige, Matthew, Taniguchi, Tadahiro, and Kawahara, Yoshihiro
- Subjects
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POSTURE , *ROBOT hands , *VISUAL learning , *PATH integrals , *REGRESSION analysis - Abstract
Although tendon-driven anthropomorphic robot hands have the potential to achieve human-level dexterity, controlling them is a great challenge owing to their mechanical complexities. Therefore, investigating human-hand control strategies is of the utmost importance. An important skill that enables the versatile manipulation ability of humans is visual posturing, i.e. the skill to make arbitrary hand postures based solely on visual observation. Visual posturing facilitates manipulation learning by enabling visual imitation learning and reusing visually similar past experiences. Therefore, this study investigates a method to replicate visual posturing in anthropomorphic robotic hands. Visual posturing in tendon-driven hands is challenging because of the hysteresis in tendon systems, the partial observability of the problem, and the presence of many actuators owing to the antagonistic tendon arrangement. To address these challenges, we propose a method that combines a model predictive path integral, a world model, and bio-inspired muscle synergies. The evaluation in a physical tendon-driven anthropomorphic robot hand showed that the proposed method achieved better visual posturing performance than a naive regression model. We anticipate that our visual posturing method will lay the foundation for versatile manipulation controllers that can adaptively learn manipulation tasks, similar to humans. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
9. Multi-view dreaming: multi-view world model with contrastive learning.
- Author
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Kinose, Akira, Okumura, Ryo, Okada, Masashi, and Taniguchi, Tadahiro
- Subjects
REINFORCEMENT learning ,ROBOT control systems ,GAUSSIAN distribution ,DISTRIBUTION (Probability theory) - Abstract
In this paper, we propose Multi-View Dreaming, a novel reinforcement learning agent for integrated recognition and control from multi-view observations by extending Dreaming. Most current reinforcement learning method assumes a single-view observation space, and this imposes limitations on the observed data, such as lack of spatial information and occlusions. This makes obtaining ideal observational information from the environment difficult and is a bottleneck for real-world robotics applications. In this paper, we use contrastive learning to train a shared latent space between different viewpoints and show how the Products of Experts approach can be used to integrate and control the probability distributions of latent states for multiple viewpoints. We also propose Multi-View DreamingV2, a variant of Multi-View Dreaming that uses a categorical distribution to model the latent state instead of the Gaussian distribution. Experiments show that the proposed method outperforms simple extensions of existing methods in a realistic robot control task. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
10. Metropolis-Hastings algorithm in joint-attention naming game: Experimental semiotics study
- Author
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Okumura, Ryota, Taniguchi, Tadahiro, Hagiwara, Yosinobu, and Taniguchi, Akira
- Subjects
FOS: Computer and information sciences ,Computer Science - Computation and Language ,Computer Science - Human-Computer Interaction ,Computation and Language (cs.CL) ,Human-Computer Interaction (cs.HC) - Abstract
In this study, we explore the emergence of symbols during interactions between individuals through an experimental semiotic study. Previous studies investigate how humans organize symbol systems through communication using artificially designed subjective experiments. In this study, we have focused on a joint attention-naming game (JA-NG) in which participants independently categorize objects and assign names while assuming their joint attention. In the theory of the Metropolis-Hastings naming game (MHNG), listeners accept provided names according to the acceptance probability computed using the Metropolis-Hastings (MH) algorithm. The theory of MHNG suggests that symbols emerge as an approximate decentralized Bayesian inference of signs, which is represented as a shared prior variable if the conditions of MHNG are satisfied. This study examines whether human participants exhibit behavior consistent with MHNG theory when playing 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. This result suggests that people followed the acceptance probability computed by the MH algorithm to some extent. Second, the MH-based model predicted human acceptance/rejection behavior more accurately than the other four models: Constant, Numerator, Subtraction, and Binary. This result indicates that symbol emergence in JA-NG can be explained using MHNG and is considered an approximate decentralized Bayesian inference.
- Published
- 2023
11. Recursive Metropolis-Hastings Naming Game: Symbol Emergence in a Multi-agent System based on Probabilistic Generative Models
- Author
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Inukai, Jun, Taniguchi, Tadahiro, Taniguchi, Akira, and Hagiwara, Yoshinobu
- Subjects
FOS: Computer and information sciences ,Computer Science - Machine Learning ,Computer Science - Computation and Language ,Computer Science - Multiagent Systems ,Computation and Language (cs.CL) ,Machine Learning (cs.LG) ,Multiagent Systems (cs.MA) - Abstract
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, 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. 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.
- Published
- 2023
12. Online Re-Planning and Adaptive Parameter Update for Multi-Agent Path Finding with Stochastic Travel Times
- Author
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Kita, Atsuyoshi, Suenari, Nobuhiro, Okada, Masashi, and Taniguchi, Tadahiro
- Subjects
FOS: Computer and information sciences ,Computer Science - Robotics ,Computer Science - Multiagent Systems ,Robotics (cs.RO) ,Multiagent Systems (cs.MA) - Abstract
This study explores the problem of Multi-Agent Path Finding with continuous and stochastic travel times whose probability distribution is unknown. Our purpose is to manage a group of automated robots that provide package delivery services in a building where pedestrians and a wide variety of robots coexist, such as delivery services in office buildings, hospitals, and apartments. It is often the case with these real-world applications that the time required for the robots to traverse a corridor takes a continuous value and is randomly distributed, and the prior knowledge of the probability distribution of the travel time is limited. Multi-Agent Path Finding has been widely studied and applied to robot management systems; however, automating the robot operation in such environments remains difficult. We propose 1) online re-planning to update the action plan of robots while it is executed, and 2) parameter update to estimate the probability distribution of travel time using Bayesian inference as the delay is observed. We use a greedy heuristic to obtain solutions in a limited computation time. Through simulations, we empirically compare the performance of our method to those of existing methods in terms of the conflict probability and the actual travel time of robots. The simulation results indicate that the proposed method can find travel paths with at least 50% fewer conflicts and a shorter actual total travel time than existing methods. The proposed method requires a small number of trials to achieve the performance because the parameter update is prioritized on the important edges for path planning, thereby satisfying the requirements of quick implementation of robust planning of automated delivery services., 9 pages, 5 figures
- Published
- 2023
13. World models and predictive coding for cognitive and developmental robotics: frontiers and challenges.
- Author
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Taniguchi, Tadahiro, Murata, Shingo, Suzuki, Masahiro, Ognibene, Dimitri, Lanillos, Pablo, Ugur, Emre, Jamone, Lorenzo, Nakamura, Tomoaki, Ciria, Alejandra, Lara, Bruno, and Pezzulo, Giovanni
- Subjects
- *
COGNITIVE robotics , *PREDICTION models , *ARTIFICIAL intelligence , *COGNITIVE learning , *AUTONOMOUS robots , *SOCIAL robots , *ROBOTS - Abstract
Creating autonomous robots that can actively explore the environment, acquire knowledge and learn skills continuously is the ultimate achievement envisioned in cognitive and developmental robotics. Importantly, if the aim is to create robots that can continuously develop through interactions with their environment, their learning processes should be based on interactions with their physical and social world in the manner of human learning and cognitive development. Based on this context, in this paper, we focus on the two concepts of world models and predictive coding. Recently, world models have attracted renewed attention as a topic of considerable interest in artificial intelligence. Cognitive systems learn world models to better predict future sensory observations and optimize their policies, i.e. controllers. Alternatively, in neuroscience, predictive coding proposes that the brain continuously predicts its inputs and adapts to model its own dynamics and control behavior in its environment. Both ideas may be considered as underpinning the cognitive development of robots and humans capable of continual or lifelong learning. Although many studies have been conducted on predictive coding in cognitive robotics and neurorobotics, the relationship between world model-based approaches in AI and predictive coding in robotics has rarely been discussed. Therefore, in this paper, we clarify the definitions, relationships, and status of current research on these topics, as well as missing pieces of world models and predictive coding in conjunction with crucially related concepts such as the free-energy principle and active inference in the context of cognitive and developmental robotics. Furthermore, we outline the frontiers and challenges involved in world models and predictive coding toward the further integration of AI and robotics, as well as the creation of robots with real cognitive and developmental capabilities in the future. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
14. Active exploration based on information gain by particle filter for efficient spatial concept formation.
- Author
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Taniguchi, Akira, Tabuchi, Yoshiki, Ishikawa, Tomochika, El Hafi, Lotfi, Hagiwara, Yoshinobu, and Taniguchi, Tadahiro
- Subjects
PROBABILISTIC generative models ,AUTONOMOUS robots ,SPATIAL filters ,RECOMMENDER systems ,MOBILE robots ,INFORMATION filtering ,BAYESIAN field theory - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
15. Active Exploration based on Information Gain by Particle Filter for Efficient Spatial Concept Formation
- Author
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Taniguchi, Akira, Tabuchi, Yoshiki, Ishikawa, Tomochika, Hafi, Lotfi El, Hagiwara, Yoshinobu, and Taniguchi, Tadahiro
- Subjects
FOS: Computer and information sciences ,Computer Science - Robotics ,Artificial Intelligence (cs.AI) ,Computer Science - Artificial Intelligence ,Robotics (cs.RO) - Abstract
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., Accepted to Advanced Robotics
- Published
- 2022
16. Emergent Communication through Metropolis-Hastings Naming Game with Deep Generative Models
- Author
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Taniguchi, Tadahiro, Yoshida, Yuto, Taniguchi, Akira, and Hagiwara, Yoshinobu
- Subjects
FOS: Computer and information sciences ,Artificial Intelligence (cs.AI) ,Computer Science - Computation and Language ,Computer Science - Artificial Intelligence ,Computation and Language (cs.CL) - Abstract
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. 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. 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., 23 pages, 12 figures
- Published
- 2022
17. Representation Uncertainty in Self-Supervised Learning as Variational Inference
- Author
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Nakamura, Hiroki, Okada, Masashi, and Taniguchi, Tadahiro
- Subjects
FOS: Computer and information sciences ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition - Abstract
In this paper, a novel self-supervised learning (SSL) method is proposed, which learns not only representations but also representations uncertainties by considering SSL in terms of variational inference. SSL is a method of learning representation without labels by maximizing the similarity between image representations of different augmented views of the same image. Variational autoencoder (VAE) is an unsupervised representation learning method that trains a probabilistic generative model with variational inference. VAE and SSL can learn representations without labels, but the relationship between VAE and SSL has not been revealed. In this paper, the theoretical relationship between SSL and variational inference is clarified. In addition, variational inference SimSiam (VI-SimSiam) is proposed, which can predict the representation uncertainty by interpreting SimSiam with variational inference and defining the latent space distribution. The experiment qualitatively showed that VISimSiam could learn uncertainty by comparing input images and predicted uncertainties. We also revealed a relationship between estimated uncertainty and classification accuracy., 15 pages, 12 figures, work in progress
- Published
- 2022
18. Hierarchical Path-planning from Speech Instructions with Spatial Concept-based Topometric Semantic Mapping
- Author
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Taniguchi, Akira, Ito, Shuya, and Taniguchi, Tadahiro
- Subjects
FOS: Computer and information sciences ,Computer Science - Robotics ,Artificial Intelligence (cs.AI) ,Computer Science - Artificial Intelligence ,Robotics (cs.RO) - Abstract
Navigating to destinations using human speech instructions is essential for autonomous mobile robots operating in the real world. Although robots can take different paths toward the same goal, the shortest path is not always optimal. A desired approach is to flexibly accommodate waypoint specifications, planning a better alternative path, even with detours. Furthermore, robots require real-time inference capabilities. Spatial representations include semantic, topological, and metric levels, each capturing different aspects of the environment. This study aims to realize a hierarchical spatial representation by a topometric semantic map and path planning with speech instructions, including waypoints. We propose SpCoTMHP, a hierarchical path-planning method that utilizes multimodal spatial concepts, incorporating place connectivity. This approach provides a novel integrated probabilistic generative model and fast approximate inference, with interaction among the hierarchy levels. A formulation based on control as probabilistic inference theoretically supports the proposed path planning. Navigation experiments using speech instruction with a waypoint demonstrated the performance improvement of path planning, WN-SPL by 0.589, and reduced computation time by 7.14 sec compared to conventional methods. Hierarchical spatial representations offer a mutually understandable form for humans and robots, enabling language-based navigation tasks.
- Published
- 2022
19. Multi-View Dreaming: Multi-View World Model with Contrastive Learning
- Author
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Kinose, Akira, Okada, Masashi, Okumura, Ryo, and Taniguchi, Tadahiro
- Subjects
FOS: Computer and information sciences ,Computer Science - Robotics ,Artificial Intelligence (cs.AI) ,Computer Science - Artificial Intelligence ,FOS: Electrical engineering, electronic engineering, information engineering ,Systems and Control (eess.SY) ,Robotics (cs.RO) ,Electrical Engineering and Systems Science - Systems and Control - Abstract
In this paper, we propose Multi-View Dreaming, a novel reinforcement learning agent for integrated recognition and control from multi-view observations by extending Dreaming. Most current reinforcement learning method assumes a single-view observation space, and this imposes limitations on the observed data, such as lack of spatial information and occlusions. This makes obtaining ideal observational information from the environment difficult and is a bottleneck for real-world robotics applications. In this paper, we use contrastive learning to train a shared latent space between different viewpoints, and show how the Products of Experts approach can be used to integrate and control the probability distributions of latent states for multiple viewpoints. We also propose Multi-View DreamingV2, a variant of Multi-View Dreaming that uses a categorical distribution to model the latent state instead of the Gaussian distribution. Experiments show that the proposed method outperforms simple extensions of existing methods in a realistic robot control task., 7 pages, 8 figures
- Published
- 2022
20. Unsupervised Multimodal Word Discovery based on Double Articulation Analysis with Co-occurrence cues
- Author
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Taniguchi, Akira, Murakami, Hiroaki, Ozaki, Ryo, and Taniguchi, Tadahiro
- Subjects
FOS: Computer and information sciences ,Computer Science - Robotics ,Artificial Intelligence (cs.AI) ,Computer Science - Computation and Language ,Computer Science - Artificial Intelligence ,Computation and Language (cs.CL) ,Robotics (cs.RO) - Abstract
Human infants acquire their verbal lexicon from minimal prior knowledge of language based on the statistical properties of phonological distributions and the co-occurrence of other sensory stimuli. In this study, we propose a novel fully unsupervised learning method discovering speech units by utilizing phonological information as a distributional cue and object information as a co-occurrence cue. The proposed method can not only (1) acquire words and phonemes from speech signals using unsupervised learning, but can also (2) utilize object information based on multiple modalities (i.e., 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 the experiment, the proposed method showed higher word discovery performance than the baseline methods. In particular, words that expressed the characteristics of the object (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. When the weight of the word modality was increased, the performance was further improved compared to the fixed condition.
- Published
- 2022
21. Simultaneous estimation of role and response strategy in human-robot role-reversal imitation learning
- Author
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Taniguchi, Tadahiro, Nakanishi, Hiroto, and Iwahashi, Naoto
- Published
- 2010
- Full Text
- View/download PDF
22. Implicit Estimation of Another's Intention Based on Modular Reinforcement Learning
- Author
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Taniguchi, Tadahiro, Ogawa, Kenji, and Sawaragi, Tetsuo
- Abstract
We described a framework for implicitly estimating another's intentions based on modular reinforcement learning. We applied the framework to a truckpushing task by two agents as a concrete example. In the experiment, the Follower agent could perceive changes in the Leader's intentions and estimate his intentions without observing any explicit signs on any action outputs from the Leader. This demonstrated that autonomous agents can cooperatively achieve a task without any explicit communication. Self-enclosed autonomous agents can indirectly perceive the other's changes in intentions from changes in their surrounding environment. It is revealed that multiple internal models help an autonomous agent to achieve collaborative task. In the context of artificial intelligence, "symbol grounding problem" is considered as an important problem. The problem deals with how robots and people can relate their symbolic system to their physical and embodied experiences. The symbolic system mentioned here is also used in communication, usually. Takamuku et al. presented a system for lexicon acquisition through behavior learning which is based on a modified multimodule reinforcement. The robot in their work is able to automatically associate words to objects with various visual features based on similarities in features of dynamics[8]. At the same time, Taniguchi et al. described an integrative learning architecture for spike timingdependent plasticity (STDP) and the reinforcement learning schemata model (RLSM) [12, 11]. The learning architecture enables an autonomous robot to acquire behavioral concepts and signs representing the situation where the robot should initiate the behavior. They called this process "symbol emergence." The symbolic system plays a important role in human social communication.They also utilize modular learning architecture to describe the process of symbol organization. However, they treat bottomup organization of "explicit symbols," which is assumed to be used explicit communication. In many researches, "symbolic communication" means exchanging discrete signals. However, the essential point of symbolic communication is not such an externalized signs, but an adaptive formation of "interpretant" from the viewpoint of Peirce's semiotics. Therefore, we focus on the implicit communication and its bottom-up process of organization. However, the system we treated in this chapter is constrained to some extent. This framework for implicit estimates does not always work well. If the system does not satisfy the assumptions made in Section 4, the framework is not guaranteed to work. The Leader's policies are fixed when the Follower agent is learning its policies, predictors, and network connections in our framework. The model described in this chapter may not work in the simultaneous multi-agent reinforcement learning environment. We intend to take these into account in future work.
- Published
- 2021
23. Editorial: Language and Robotics
- Author
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Taniguchi, Tadahiro, Horii, Takato, Hinaut, Xavier, Spranger, Michael, Mochihashi, Daichi, and Nagai, Takayuki
- Subjects
Robotics and AI ,symbol grounding in robotics ,Editorial ,multimodal communication ,language acquisition by robots ,deep learning for robotics ,emergence of communication ,symbol emergence in robotics ,concept formation - Published
- 2021
24. Autonomous service robot for human-aware restock, straightening and disposal tasks in retail automation.
- Author
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Garcia Ricardez, Gustavo A., Uriguen Eljuri, Pedro M., Kamemura, Yuta, Yokota, Shiori, Kugou, Noriyuki, Asama, Yoshitatsu, Wang, Ziyu, Kumamoto, Hikaru, Yoshimoto, Kotaro, Chan, Wai Y., Nagatani, Tomoki, Tulathum, Pattaraporn, Usawalertkamol, Bunyapon, El Hafi, Lotfi, Ikeuchi, Hiroki, Yamamoto, Masaki, Takamatsu, Jun, Taniguchi, Tadahiro, and Ogasawara, Tsukasa
- Subjects
AUTONOMOUS robots ,AUTOMATION ,ROBOTICS competitions ,CONVENIENCE stores ,INTERNATIONAL competition ,LUNCHBOXES - Abstract
The workforce shortage in the service industry, recently highlighted by the pandemic, has increased the need for automation. We propose an autonomous robot to fulfill this purpose. Our mobile manipulator includes an extendable and compliant end effector design, as well as a custom-made automated shelf, and it is capable of manipulating food products such as lunch boxes, while traversing narrow spaces and reacting to human interventions. We benchmarked the solution in the international robotics competition Future Convenience Store Challenge (FCSC) where we obtained the first place in the 2020 edition, as well as in a laboratory setting, both situated in a convenience store scenario. We reported the results evaluated in terms of the score of the FCSC 2020 and further discussed the real-world applicability of the current system and open challenges. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
25. Versatile cleaning service robot based on a mobile manipulator with tool switching for liquids and garbage removal in restrooms.
- Author
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Wakayama, Tatsuhiro, Fujiura, Eri, Yamaguchi, Mari, Yoshida, Naoki, Inoue, Tomoaki, Ikeuchi, Hiroki, Yamamoto, Masaki, El Hafi, Lotfi, Garcia Ricardez, Gustavo A., Takamatsu, Jun, Taniguchi, Tadahiro, and Ogasawara, Tsukasa
- Subjects
RESTROOMS ,MOBILE robots ,ORGANIC wastes ,SYSTEM integration ,MOBILE hospitals ,CONVENIENCE stores - Abstract
In recent years, the labor shortage has become a significant problem in Japan and other countries due to aging societies. However, service robots can play a decisive role in relieving human workers by performing various household and assistive tasks. Restroom cleaning is one of such challenging tasks that involve performing motion planning in a constrained restroom setting. In this study, we propose a mobile manipulator to perform various tasks related to restroom cleaning. Our key contributions include system integration of multiple tools on an arm with high DoF mounted on a mobile, omni-directional platform capable of versatile service cleaning and with extended reachability. We evaluate the performance of our system with the competition setting used for the restroom cleaning task of the Future Convenience Store Challenge at the World Robot Summit 2020, where we obtained the 1st Place. The proposed system successfully completed all the competition tasks within the time limit and could remove the liquid with a removal rate of 96%. The proposed system could also dispose of most garbage and got an average garbage disposal rate of 90%. Further experiments confirmed the scores obtained in the competition with an even higher liquid removal rate of 98%. [ABSTRACT FROM AUTHOR]
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- 2022
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26. Incremental acquisition of multiple nonlinear forward models based on differentiation process of schema model
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Taniguchi, Tadahiro and Sawaragi, Tetsuo
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- 2008
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27. Map completion from partial observation using the global structure of multiple environmental maps.
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Katsumata, Yuki, Kanechika, Akinori, Taniguchi, Akira, El Hafi, Lotfi, Hagiwara, Yoshinobu, and Taniguchi, Tadahiro
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ENVIRONMENTAL mapping ,GENERATIVE adversarial networks ,PROBABILISTIC generative models ,MAPS ,AUTONOMOUS robots ,MOBILE robots - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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28. Multiagent multimodal categorization for symbol emergence: emergent communication via interpersonal cross-modal inference.
- Author
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Hagiwara, Yoshinobu, Furukawa, Kazuma, Taniguchi, Akira, and Taniguchi, Tadahiro
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INTERPERSONAL communication ,MARKOV chain Monte Carlo ,PROBABILISTIC generative models ,SIGNS & symbols - Abstract
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. [ABSTRACT FROM AUTHOR]
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- 2022
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29. Optical laser microphone for human-robot interaction: speech recognition in extremely noisy service environments.
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Fukumori, Takahiro, Cai, Chengkai, Zhang, Yutao, El Hafi, Lotfi, Hagiwara, Yoshinobu, Nishiura, Takanobu, and Taniguchi, Tadahiro
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MICROPHONES ,HUMAN-robot interaction ,SPEECH perception ,LASER Doppler vibrometer ,LASERS ,SOUND recordings ,PHOTOACOUSTIC effect - Abstract
Domestic robots are often required to understand spoken commands in noisy environments, including service appliances' operating sounds. Most conventional domestic robots use electret condenser microphones (ECMs) to record the sound. However, the ECMs are known to be sensitive to the noise in the direction of sound arrival. The laser Doppler vibrometer (LDV), which has been widely used in the research field of measurement, has the potential to work as a new speech-input device to solve this problem. The aim of this paper is to investigate the effectiveness of using the LDV as an optical laser microphone for human-robot interaction in extremely noisy service environments. Our robot irradiates an object near a speaker with a laser and measures the vibration of the object to record the sound. We conducted three experiments to assess the performance of speech recognition using the optical laser microphone in various settings and showed stable performance in extremely noisy conditions compared with a conventional ECM. [ABSTRACT FROM AUTHOR]
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- 2022
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30. What is the role of the next generation of cognitive robotics?
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Shimoda, Shingo, Jamone, Lorenzo, Ognibene, Dimitri, Nagai, Takayuki, Sciutti, Alessandra, Costa-Garcia, Alvaro, Oseki, Yohei, and Taniguchi, Tadahiro
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COGNITIVE robotics ,SOCIAL robots ,COGNITIVE ability ,LIFE partners ,ROBOTS - Abstract
Social demand for robots to be our partners in daily life has been rapidly increasing. Cognitive robotics should play a major role in making robots our partners. To discuss the role of cognitive robotics, we organized the round table in December 2020. This review paper aimed at clarifying the role of cognitive robotics summarizing the discussion in the round table. The round table noted that the existence of uncertainty in the continuous control loop is a source of the need for cognitive robots and is the key factor that distinguishes cognitive robotics from the cognitive system in other fields. This paper summarized the discussion focusing on the creation of several cognitive functions without stopping even if the robots face novel uncertainty in daily life. We discussed information generalization, active sensing, prediction, and language communication as the necessary functions for future cognitive robots. One of the conclusions of the discussion is the importance of setting primitive but concrete targets for cognitive robotics research as cognitive robotics problems. We should continue to discuss the setting of these targets as a grand challenge for cognitive robotics. [ABSTRACT FROM AUTHOR]
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- 2022
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31. Hierarchical Bayesian model for the transfer of knowledge on spatial concepts based on multimodal information.
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Hagiwara, Yoshinobu, Taguchi, Keishiro, Ishibushi, Satoshi, Taniguchi, Akira, and Taniguchi, Tadahiro
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KNOWLEDGE transfer ,HUMAN-robot interaction ,PRIOR learning - Abstract
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. [ABSTRACT FROM AUTHOR]
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- 2022
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32. Unsupervised lexical acquisition of relative spatial concepts using spoken user utterances.
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Sagara, Rikunari, Taguchi, Ryo, Taniguchi, Akira, Taniguchi, Tadahiro, Hattori, Koosuke, Hoguro, Masahiro, and Umezaki, Taizo
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HUMAN-machine relationship ,SOCIAL interaction ,SPEECH perception ,HUMAN-robot interaction - Abstract
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. [ABSTRACT FROM AUTHOR]
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- 2022
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33. Variational Inference MPC for Bayesian Model-based Reinforcement Learning
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Okada, Masashi and Taniguchi, Tadahiro
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FOS: Computer and information sciences ,Computer Science - Machine Learning ,Statistics - Machine Learning ,FOS: Electrical engineering, electronic engineering, information engineering ,Machine Learning (stat.ML) ,Systems and Control (eess.SY) ,Electrical Engineering and Systems Science - Systems and Control ,Machine Learning (cs.LG) - Abstract
In recent studies on model-based reinforcement learning (MBRL), incorporating uncertainty in forward dynamics is a state-of-the-art strategy to enhance learning performance, making MBRLs competitive to cutting-edge model free methods, especially in simulated robotics tasks. Probabilistic ensembles with trajectory sampling (PETS) is a leading type of MBRL, which employs Bayesian inference to dynamics modeling and model predictive control (MPC) with stochastic optimization via the cross entropy method (CEM). In this paper, we propose a novel extension to the uncertainty-aware MBRL. Our main contributions are twofold: Firstly, we introduce a variational inference MPC, which reformulates various stochastic methods, including CEM, in a Bayesian fashion. Secondly, we propose a novel instance of the framework, called probabilistic action ensembles with trajectory sampling (PaETS). As a result, our Bayesian MBRL can involve multimodal uncertainties both in dynamics and optimal trajectories. In comparison to PETS, our method consistently improves asymptotic performance on several challenging locomotion tasks., Accepted to CoRL2019. Camera-ready ver
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- 2019
34. Towards Understanding Syntactic Structure of Language in Human-Robot Interaction
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ALY, Amir, Taniguchi, Tadahiro, Mochihashi, Daichi, Robotique et Vision (RV), Unité d'Informatique et d'Ingénierie des Systèmes (U2IS), École Nationale Supérieure de Techniques Avancées (ENSTA Paris)-École Nationale Supérieure de Techniques Avancées (ENSTA Paris), Ritsumeikan University, and ALY, Amir
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[INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO] ,[INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO] - Abstract
International audience; Robots are progressively moving into spaces that have been primarily shaped by human agency; they collaborate with human users in different tasks that require them to understand human language so as to behave appropriately in space. To this end, a stubborn challenge that we address in this paper is inferring the syntactic structure of language, which embraces grounding parts of speech (e.g., nouns, verbs, and prepositions) through visual perception, and induction of Combinatory Categorial Grammar (CCG) in situated human-robot interaction. This could pave the way towards making a robot able to understand the syntactic relationships between words (i.e., understand phrases), and consequently the meaning of human instructions during interaction, which is a future scope of this current study. I. INTRODUCTION Creating interactive social robots able to collaborate with human users in different tasks requires high-level spatial intelligence that could make them able to discover and interact with their surroundings. Developing this spatial intelligence involves grounding language (action verbs, object characteristics (i.e., color and geometry), and spatial prepositions) and the underlying syntactic structure through sensory information so as to make a robot able to understand human instructions in the physical world. Understanding syntactic structure of language has been intensively investigated in the literature of cognitive robotics and computational linguistics. In cognitive robotics, different research studies proposed computational models for grounding nouns, verbs, adjectives, and prepositions encoding spatial relationships between objects [1, 2, 22, 26, 38]. However, they have not investigated grammar understanding at the phrase level, which constitutes a higher level than grounding words through perception. Meanwhile, in computational linguistics, recent studies presented models for inducing combinatory syntactic structure of language [5, 15]; however, they used annotated databases for grammar induction where each word has a corresponding syntactic tag (as a noun, verb, etc.). This last point illustrates the important role that cognitive robotics could play in grammar induction through grounding parts of speech in visual perception so as to allow for learning the latent syntactic structure of phrases in a developmentally plausible manner. In this study, we build on the model of Bisk and Hockenmaier [5] for grammar induction, and propose an extended probabilistic
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- 2018
35. Towards Intelligent Social Robots: From Naive Robots to Robot Sapiens
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ALY, Amir, Griffiths, Sascha, Nitsch, Verena, Pastra, Katerina, Taniguchi, Tadahiro, Ritsumeikan University, University of Hamburg, and Universität der Bundeswehr München [Neubiberg]
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[INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO] ,[INFO]Computer Science [cs] ,ComputingMilieux_MISCELLANEOUS - Abstract
International audience
- Published
- 2018
36. Semiotically adaptive cognition: toward the realization of remotely-operated service robots for the new normal symbiotic society.
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Taniguchi, Tadahiro, El Hafi, Lotfi, Hagiwara, Yoshinobu, Taniguchi, Akira, Shimada, Nobutaka, and Nishiura, Takanobu
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- *
COGNITION , *ROBOTS , *ROBOTICS , *LOCAL knowledge , *COGNITIVE robotics , *HOSPITAL quality control - Abstract
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 the so-called new normal. However, it is evident that remotely-operated robots must have partial autonomy and the capability to learn and use local semiotic knowledge. In this paper, we argue that the development of semiotically adaptive cognitive systems is key to the installation of service robotics technologies in our service environments. To achieve this goal, we describe three challenges: the learning of local knowledge, the acceleration of onsite and online learning, and the augmentation of human–robot interactions. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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37. Autonomous planning based on spatial concepts to tidy up home environments with service robots.
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Taniguchi, Akira, Isobe, Shota, El Hafi, Lotfi, Hagiwara, Yoshinobu, and Taniguchi, Tadahiro
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AUTONOMOUS robots ,HOME environment ,PROBABILISTIC generative models ,ROBOTS ,MULTIMODAL user interfaces ,ROBOTICS competitions - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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38. Towards Understanding Language through Perception in Situated Human-Robot Interaction: From Word Grounding to Grammar Induction
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ALY, Amir, Taniguchi, Tadahiro, Ritsumeikan University, and ALY, Amir
- Subjects
FOS: Computer and information sciences ,Computer Science - Computation and Language ,Artificial Intelligence (cs.AI) ,Computer Science - Artificial Intelligence ,[INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO] ,[INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO] ,Computation and Language (cs.CL) - Abstract
Robots are widely collaborating with human users in diferent tasks that require high-level cognitive functions to make them able to discover the surrounding environment. A difcult challenge that we briefy highlight in this short paper is inferring the latent grammatical structure of language, which includes grounding parts of speech (e.g., verbs, nouns, adjectives, and prepositions) through visual perception, and induction of Combinatory Categorial Grammar (CCG) for phrases. This paves the way towards grounding phrases so as to make a robot able to understand human instructions appropriately during interaction., Comment: Proceedings of the International Conference on Social Cognition in Humans and Robots (socSMCs), Germany, 2018
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- 2018
39. A Probabilistic Framework for Comparing Syntactic and Semantic Grounding of Synonyms through Cross-Situational Learning
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Roesler, Oliver, Aly, Amir, Taniguchi, Tadahiro, Hayashi, Yoshikatsu, and ALY, Amir
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[INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO] - Abstract
Natural human-robot interaction requires robots to link words to objects and actions through grounding. Although grounding has been investigated in previous studies, none of them considered grounding of synonyms. In this paper, we try to fill this gap by introducing a Bayesian learning model for grounding synonymous object and action names using cross-situational learning. Three different word representations are employed with the probabilistic model and evaluated according to their grounding performance. Words are grounded through geometric characteristics of objects and kinematic features of the robot joints during action execution. An interaction experiment between a human tutor and HSR robot is used to evaluate the proposed model. The results show that representing words by syntactic and/or semantic information achieves worse grounding results than representing them by unique numbers.
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- 2018
40. A Bayesian Approach to Phrase Understanding through Cross-Situational Learning
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Aly, Amir, Taniguchi, Tadahiro, Mochihashi, Daichi, and ALY, Amir
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[INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO] - Abstract
In this paper, we present an unsupervised probabilistic framework to grounding words (e.g., nouns, verbs, adjectives, and prepositions) through visual perception, and we discuss grammar induction in situated human-robot interaction with the objective of making a robot able to understand the underlying syntactic structure of human instructions so as to collaborate with users in space efficiently.
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- 2018
41. Panacea: Visual exploration system for analyzing trends in annual recruitment using time-varying graphs.
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Yokoyama, Toshiyuki T., Okada, Masashi, and Taniguchi, Tadahiro
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HUMAN resources departments ,TIME-varying systems ,STATISTICAL correlation ,DATA analysis ,DYNAMIC models - Abstract
Annual recruitment data of new graduates are manually analyzed by human resources (HR) specialists in industries, which signifies the need to evaluate the recruitment strategy of HR specialists. Different job seekers send applications to companies every year. The relationships between applicants' attributes (e.g., English skill or academic credentials) can be used to analyze the changes in recruitment trends across multiple years. However, most attributes are unnormalized and thus require thorough preprocessing. Such unnormalized data hinder effective comparison of the relationship between applicants in the early stage of data analysis. Thus, a visual exploration system is highly needed to gain insight from the overview of the relationship among applicant qualifications across multiple years. In this study, we propose the Polarizing Attributes for Network Analysis of Correlation on Entities Association (Panacea) visualization system. The proposed system integrates a time-varying graph model and dynamic graph visualization for heterogeneous tabular data. Using this system, HR specialists can interactively inspect the relationships between two attributes of prospective employees across multiple years. Further, we demonstrate the usability of Panacea with representative examples for finding hidden trends in real-world datasets, and we discuss feedback from HR specialists obtained throughout Panacea's development. The proposed Panacea system enables HR specialists to visually explore the annual recruitment of new graduates. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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42. Spatial concept-based navigation with human speech instructions via probabilistic inference on Bayesian generative model.
- Author
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Taniguchi, Akira, Hagiwara, Yoshinobu, Taniguchi, Tadahiro, and Inamura, Tetsunari
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ROBOTICS ,REINFORCEMENT learning ,MOBILE robots ,SPEECH ,AUTOMATIC speech recognition ,TEACHING ,ROBOT vision - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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43. Integration of imitation learning using GAIL and reinforcement learning using task-achievement rewards via probabilistic graphical model.
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Kinose, Akira and Taniguchi, Tadahiro
- Subjects
- *
REINFORCEMENT learning , *PROBABILISTIC number theory , *MARKOV processes , *HEURISTIC - Abstract
The integration of reinforcement learning (RL) and imitation learning (IL) is an important problem that has long been studied in the field of intelligent robotics. RL optimizes policies to maximize the cumulative reward, whereas IL attempts to extract general knowledge about the trajectories demonstrated by experts, i.e, demonstrators. Because each has its own drawbacks, many methods combining them and compensating for each set of drawbacks have been explored thus far. However, many of these methods are heuristic and do not have a solid theoretical basis. This paper presents a new theory for integrating RL and IL by extending the probabilistic graphical model (PGM) framework for RL, control as inference. We develop a new PGM for RL with multiple types of rewards, called probabilistic graphical model for Markov decision processes with multiple optimality emissions (pMDP-MO). Furthermore, we demonstrate that the integrated learning method of RL and IL can be formulated as a probabilistic inference of policies on pMDP-MO by considering the discriminator in generative adversarial imitation learning (GAIL) as an additional optimality emission. We adapt the GAIL and task-achievement reward to our proposed framework, achieving significantly better performance than policies trained with baseline methods. [ABSTRACT FROM AUTHOR]
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- 2020
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44. Special issue on world models and predictive coding in robotics (part II).
- Author
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Taniguchi, Tadahiro, Ognibene, Dimitri, Jamone, Lorenzo, Ugur, Emre, Lanillos, Pablo, Ciria, Alejandra, Suzuki, Masahiro, Murata, Shingo, Nakata, Yoshihiro, and Nakamura, Tomoaki
- Subjects
- *
PREDICTION models , *COGNITIVE robotics , *PROBABILISTIC generative models , *ROBOTICS - Abstract
We are pleased to announce the special issue on World Models and Predictive Coding in Robotics. This library will accelerate robotics research using such models, including world models. [Extracted from the article]
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- 2023
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45. Editorial: Machine Learning Methods for High-Level Cognitive Capabilities in Robotics.
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Taniguchi, Tadahiro, Ugur, Emre, Ogata, Tetsuya, Nagai, Takayuki, and Demiris, Yiannis
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COGNITIVE robotics ,MACHINE learning ,PROBABILISTIC generative models ,ROBOT motion ,ROBOT programming ,HUMAN behavior - Abstract
Keywords: machine leading; cognitive robotics; language acquisition; neural networks; cognitive architecture; probabilistic models; robot learning Following the successfully organized session "the Workshop on Machine Learning Methods for High-Level Cognitive Capabilities in Robotics 2016" held in IEEE-IROS 2016[1], we organized this Research Topic. Nakajo et al. proposed a machine learning method for viewpoint transformation and action mapping using a neural network having encoder-decoder architecture, i.e., sequence to sequence. Machine leading, cognitive robotics, language acquisition, neural networks, cognitive architecture, probabilistic models, robot learning. [Extracted from the article]
- Published
- 2019
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46. SERKET: An Architecture for Connecting Stochastic Models to Realize a Large-Scale Cognitive Model.
- Author
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Nakamura, Tomoaki, Nagai, Takayuki, and Taniguchi, Tadahiro
- Subjects
ARTIFICIAL intelligence ,ROBOTICS ,ARTIFICIAL neural networks - Abstract
To realize human-like robot intelligence, a large-scale cognitive architecture is required for robots to understand their environment through a variety of sensors with which they are equipped. In this paper, we propose a novel framework named Serket that enables the construction of a large-scale generative model and its inferences easily by connecting sub-modules to allow the robots to acquire various capabilities through interaction with their environment and others. We consider that large-scale cognitive models can be constructed by connecting smaller fundamental models hierarchically while maintaining their programmatic independence. Moreover, the connected modules are dependent on each other and their parameters must be optimized as a whole. Conventionally, the equations for parameter estimation have to be derived and implemented depending on the models. However, it has become harder to derive and implement equations of large-scale models. Thus, in this paper, we propose a parameter estimation method that communicates the minimum parameters between various modules while maintaining their programmatic independence. Therefore, Serket makes it easy to construct large-scale models and estimate their parameters via the connection of modules. Experimental results demonstrated that the model can be constructed by connecting modules, the parameters can be optimized as a whole, and they are comparable with the original models that we have proposed. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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- View/download PDF
47. Hierarchical Spatial Concept Formation Based on Multimodal Information for Human Support Robots.
- Author
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Hagiwara, Yoshinobu, Inoue, Masakazu, Kobayashi, Hiroyoshi, and Taniguchi, Tadahiro
- Subjects
HUMANOID robots ,OBJECT recognition (Computer vision) ,K-means clustering - Abstract
In this paper, we propose a hierarchical spatial concept formation method based on the Bayesian generative model with multimodal information e.g., vision, position and word information. Since humans have the ability to select an appropriate level of abstraction according to the situation and describe their position linguistically, e.g., "I aminmy home" and "I am in front of the table," a hierarchical structure of spatial concepts is necessary in order for human support robots to communicate smoothly with users. The proposed method enables a robot to form hierarchical spatial concepts by categorizing multimodal information using hierarchical multimodal latent Dirichlet allocation (hMLDA). Object recognition results using convolutional neural network (CNN), hierarchical k-means clustering result of self-position estimated by Monte Carlo localization (MCL), and a set of location names are used, respectively, as features in vision, position, and word information. Experiments in forming hierarchical spatial concepts and evaluating how the proposed method can predict unobserved location names and position categories are performed using a robot in the real world. Results verify that, relative to comparable baseline methods, the proposed method enables a robot to predict location names and position categories closer to predictions made by humans. As an application example of the proposed method in a home environment, a demonstration in which a human support robot moves to an instructed place based on human speech instructions is achieved based on the formed hierarchical spatial concept. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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- View/download PDF
48. Defect-Repairable Latent Feature Extraction of Driving Behavior via a Deep Sparse Autoencoder.
- Author
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Liu, Hailong, Taniguchi, Tadahiro, Takenaka, Kazuhito, and Bando, Takashi
- Subjects
- *
POINT defects , *FEATURE extraction , *TIME series analysis , *DEEP learning , *ACQUISITION of data - Abstract
Data representing driving behavior, as measured by various sensors installed in a vehicle, are collected as multi-dimensional sensor time-series data. These data often include redundant information, e.g., both the speed of wheels and the engine speed represent the velocity of the vehicle. Redundant information can be expected to complicate the data analysis, e.g., more factors need to be analyzed; even varying the levels of redundancy can influence the results of the analysis. We assume that the measured multi-dimensional sensor time-series data of driving behavior are generated from low-dimensional data shared by the many types of one-dimensional data of which multi-dimensional time-series data are composed. Meanwhile, sensor time-series data may be defective because of sensor failure. Therefore, another important function is to reduce the negative effect of defective data when extracting low-dimensional time-series data. This study proposes a defect-repairable feature extraction method based on a deep sparse autoencoder (DSAE) to extract low-dimensional time-series data. In the experiments, we show that DSAE provides high-performance latent feature extraction for driving behavior, even for defective sensor time-series data. In addition, we show that the negative effect of defects on the driving behavior segmentation task could be reduced using the latent features extracted by DSAE. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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- View/download PDF
49. Unsupervised spatial lexical acquisition by updating a language model with place clues.
- Author
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Taniguchi, Akira, Taniguchi, Tadahiro, and Inamura, Tetsunari
- Subjects
- *
AUTOMATIC speech recognition , *LANGUAGE acquisition , *MACHINE learning , *NONPARAMETRIC estimation , *PROBLEM solving , *PHONEME (Linguistics) , *BAYESIAN analysis - Abstract
This paper describes how to achieve highly accurate unsupervised spatial lexical acquisition from speech-recognition results including phoneme recognition errors. In most research into lexical acquisition, the robot has no pre-existing lexical knowledge. The robot acquires sequences of some phonemes as words from continuous speech signals. In a previous study, we proposed a nonparametric Bayesian spatial concept acquisition method (SpCoA) that integrates the robot’s position and words obtained by unsupervised word segmentation from uncertain syllable recognition results. However, SpCoA has a very critical problem to be solved in lexical acquisition; the boundaries of word segmentation are incorrect in many cases because of many phoneme recognition errors. Therefore, we propose an unsupervised machine learning method (SpCoA++) for the robust lexical acquisition of novel words relating to places visited by the robot. The proposed SpCoA++ method performs an iterative estimation of learning spatial concepts and updating a language model using place information. SpCoA++ can select a candidate including many words that better represent places from multiple word-segmentation results by maximizing the mutual information between segmented words and spatial concepts. The experimental results demonstrate a significant improvement of the phoneme accuracy rate of learned words relating to place in the proposed method by word-segmentation results based on place information, in comparison to the conventional methods. We indicate that the proposed method enables the robot to acquire words from speech signals more accurately, and improves the estimation accuracy of the spatial concepts. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
50. Implicit Estimation of Another's Intention Based on Modular Reinforcement Learning
- Author
-
Taniguchi, Tadahiro
- Subjects
Computers / Artificial Intelligence - Abstract
Implicit Estimation of Another's Intention Based on Modular Reinforcement Learning
- Published
- 2009
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