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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]

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]

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]

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. [ABSTRACT FROM AUTHOR]

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]

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]

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. [ABSTRACT FROM AUTHOR]

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]

We are pleased to announce the special issue on World Models and Predictive Coding in Robotics. The pursuit of autonomous cognitive-developmental robots has long been a high aspiration within the field of robotics, and creating cognitive dynamics that enable robots to learn and adapt through sensorimotor interactions is a key challenge in cognitive and developmental robotics. [Extracted from the article]

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]

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]

Innovations in artificial intelligence have opened the door to the next generation of cognitive robotics. This special issue aims to not only report the latest research results in these fields, but also to further discuss the future cognitive robotics in the 2020s and beyond. We are pleased to announce the special issue on Symbol Emergence in Robotics and Cognitive Systems. [Extracted from the article]

We are pleased to announce the special issue on Symbol Emergence in Robotics and Cognitive Systems. This special issue aims to not only report the latest research results in these fields but also to further discuss the future cognitive robotics in the 2020s and beyond. [Extracted from the article]

A sequence prediction method for driving behavior data is proposed in this paper. The proposed method can predict a longer latent state sequence of driving behavior data than conventional sequence prediction methods. The proposed method is derived by focusing on the double articulation structure latently embedded in driving behavior data. The double articulation structure is a two-layer hierarchical structure originally found in spoken language, i.e., a sentence is a sequence of words and a word is a sequence of letters. Analogously, we assume that driving behavior data comprise a sequence of driving words and a driving word is a sequence of driving letters. The sequence prediction method is obtained by extending a nonparametric Bayesian unsupervised morphological analyzer using a nested Pitman-Yor language model (NPYLM), which was originally proposed in the natural language processing field. This extension allows the proposed method to analyze incomplete sequences of latent states of driving behavior and to predict subsequent latent states on the basis of a maximum a posteriori criterion. The extension requires a marginalization technique over an infinite number of possible driving words. We derived such a technique on the basis of several characteristics of the NPYLM. We evaluated this proposed sequence prediction method using three types of data: 1) synthetic data; 2) data from test drives around a driving course at a factory; and 3) data from drives on a public thoroughfare. The results showed that the proposed method made better long-term predictions than did the previous methods. [ABSTRACT FROM PUBLISHER]

Direct word discovery from audio speech signals is a very difficult and challenging problem for a developmental robot. Human infants are able to discover words directly from speech signals, and, to understand human infants' developmental capability using a constructive approach, it is very important to build a machine learning system that can acquire knowledge about words and phonemes, i.e. a language model and an acoustic model, autonomously in an unsupervised manner. To achieve this, the nonparametric Bayesian double articulation analyzer (NPB-DAA) with the deep sparse autoencoder (DSAE) is proposed in this paper. The NPB-DAA has been proposed to achieve totally unsupervised direct word discovery from speech signals. However, the performance was still unsatisfactory, although it outperformed pre-existing unsupervised learning methods. In this paper, we integrate the NPB-DAA with the DSAE, which is a neural network model that can be trained in an unsupervised manner, and demonstrate its performance through an experiment about direct word discovery from auditory speech signals. The experiment shows that the combinedmethod, the NPB-DAAwith the DSAE, outperforms pre-existing unsupervised learningmethods, and shows stateof- the-art performance. It is also shown that the proposed method outperforms several standard speech recognizer-based methods with true word dictionaries. [ABSTRACT FROM AUTHOR]

Humans can learn a language through physical interaction with their environment and semiotic communication with other people. It is very important to obtain a computational understanding of how humans can form symbol systems and obtain semiotic skills through their autonomous mental development. Recently, many studies have been conducted regarding the construction of robotic systems and machine learning methods that can learn a language through embodied multimodal interaction with their environment and other systems. Understanding human?-social interactions and developing a robot that can smoothly communicate with human users in the long term require an understanding of the dynamics of symbol systems. The embodied cognition and social interaction of participants gradually alter a symbol system in a constructivemanner. In this paper, we introduce a field of research called symbol emergence in robotics (SER). SER represents a constructive approach towards a symbol emergence system. The symbol emergence system is socially selforganized through both semiotic communications and physical interactions with autonomous cognitive developmental agents, i.e. humans and developmental robots. In this paper, specifically, we describe some state-of-art research topics concerning SER, such as multimodal categorization, word discovery, and double articulation analysis. They enable robots to discoverwords and their embodied meanings from raw sensory-motor information, including visual information, haptic information, auditory information, and acoustic speech signals, in a totally unsupervised manner. Finally, we suggest future directions for research in SER. [ABSTRACT FROM AUTHOR]

We propose an unsupervised motion segmentation method for enabling a robot to imitate and perform various unit motions by observing unsegmented human motion. Natural unsegmented human motion data contain various types of unit motions, such as 'waving good-bye', 'walking' and 'throwing a ball'. A robot has to segment the data and extract unit motions from the data to imitate the motions. In previous work, an ergodic hidden Markov model (HMM) was used to model unsegmented human motion. However, there are two main problems with the classical use of this model. (i) Setting an appropriate number of hidden states is difficult because how complex the motions contained in the learning data are and how many there are unknown. (ii) We did not have an effective chunking method that could chunk elemental motions into meaningful unit motions without being captured by local minima. To overcome these problems, we developed an unsupervised motion segmentation method for imitation learning using a sticky hierarchical Dirichlet process (HDP)-HMM, a nonparametric Bayesian model, and an unsupervised chunking method based on a Gibbs sampler and the minimal description length (MDL) principle of imitation learning of unsegmented human motion. We developed this chunking method to work with the sticky HDP-HMM and extract unit human motions. We conducted several experiments to evaluate this method. The proposed method could extract unit motions from unsegmented human motion data. The sticky HDP-HMM can be used to model unsegmented human motion more accurately than with a conventional HMM and simultaneously estimate the number of hidden states. We also evaluated the dependency of the HDP-HMM on the hyperparameters of the model. [ABSTRACT FROM AUTHOR]

Abstract: We introduce the schema model as an alternative computational model representing multiple internal models. The human central nervous system is believed to obtain multiple forward-inverse models. The schema model enables agents to obtain multiple nonlinear forward models incrementally. This model is based on hypothesis testing theory whereas most modular learning methods are based on a Bayesian framework. As a specific example, we describe a schema model with a normalized Gaussian network (NGSM). Simulation revealed that NGSM has two advantages over MOSAIC’s learning method: NGSM can obtain multiple models incrementally and does not depend on the initial parameters of the forward models. [Copyright &y& Elsevier]

A novel integrative learning architecture based on a reinforcement learning schemata model (RLSM) with a spike timing-dependent plasticity (STDP) network is described. This architecture models operant conditioning with discriminative stimuli in an autonomous agent engaged in multiple reinforcement learning tasks. The architecture consists of two constitutional learning architectures: RLSM and STDP. RLSM is an incremental modular reinforcement learning architecture, and it makes an autonomous agent acquire several behavioral concepts incrementally through continuous interactions with its environment and/or caregivers. STDP is a learning rule of neuronal plasticity found in cerebral cortices and the hippocampus of the human brain. STDP is a temporally asymmetric learning rule that contrasts with the Hebbian learning rule. We found that STDP enabled an autonomous robot to associate auditory input with its acquired behaviors and to select reinforcement learning modules more effectively. Auditory signals interpreted based on the acquired behaviors were revealed to correspond to 'signs' of required behaviors and incoming situations. This integrative learning architecture was evaluated in the context of on-line modular learning. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

This paper describes a computational model, called the Dirichlet process Gaussian mixture model with latent joints (DPGMM-LJ), that can find latent tree structure embedded in data distribution in an unsupervised manner. By combining DPGMM-LJ and a preexisting body map formation method, we propose a method that enables an agent having multilink body structure to discover its kinematic structure, i.e. body schema, from tactile information alone. The DPGMM-LJ is a probabilistic model based on Bayesian nonparametrics and an extension of Dirichlet process Gaussian mixture model (DPGMM). In a simulation experiment, we used a simple fetus model that had five body parts and performed structured random movements in a womb-like environment. It was shown that the method could estimate the number of body parts and kinematic structures without any preexisting knowledge in many cases. Another experiment showed that the degree of motor coordination in random movements affects the result of body schema formation strongly. It is confirmed that the accuracy rate for body schema estimation had the highest value 84.6% when the ratio of motor coordination was 0.9 in our setting. These results suggest that kinematic structure can be estimated from tactile information obtained by a fetus moving randomly in a womb without any visual information, even though its accuracy was not so high. They also suggest that a certain degree of motor coordination in random movements and the sufficient dimension of state space that represents the body map are important to estimate body schema correctly. [ABSTRACT FROM AUTHOR]

SUMMARY In this paper, we target the specific smart grid concept called 'inter-intelligent renewable energy network (i-Rene),' which is characteristically associated with photovoltaic (PV) panels, microstorage, and regional electricity markets. In this smart grid, every house has an artificial intelligence agent that learns the optimal treading strategy to minimize payments by residents. From the economic standpoint, we perform a cost-benefit analysis (CBA) of the optimal installation of PV panels and microstorage in this smart grid. To perform the CBA, we use actual measured values of power demand profiles and power production profiles, which were obtained in 2010 at Shiga, Japan. In accordance with this analysis, we also discuss the necessary conditions for achieving grid parity of the i-Rene smart grid . [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

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]

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]

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]

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]

Understanding information processing in the brain—and creating general-purpose artificial intelligence—are long-standing aspirations of scientists and engineers worldwide. The distinctive features of human intelligence are high-level cognition and control in various interactions with the world including the self, which are not defined in advance and are vary over time. The challenge of building human-like intelligent machines, as well as progress in brain science and behavioural analyses, robotics, and their associated theoretical formalisations, speaks to the importance of the world-model learning and inference. In this article, after briefly surveying the history and challenges of internal model learning and probabilistic learning, we introduce the free energy principle, which provides a useful framework within which to consider neuronal computation and probabilistic world models. Next, we showcase examples of human behaviour and cognition explained under that principle. We then describe symbol emergence in the context of probabilistic modelling, as a topic at the frontiers of cognitive robotics. Lastly, we review recent progress in creating human-like intelligence by using novel probabilistic programming languages. The striking consensus that emerges from these studies is that probabilistic descriptions of learning and inference are powerful and effective ways to create human-like artificial intelligent machines and to understand intelligence in the context of how humans interact with their world. [ABSTRACT FROM AUTHOR]

Soft/Social/Systemic (3S) Robot Technologies have great potentials for enhancing the Quality of New Normal (QoNN). Inamura et al. proposed a VR platform for human-robot interactions among multiple human users and a virtual robot and showed its case study of online robot competition. [Extracted from the article]

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]