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Recently, there has been an increase in the importance of community-centric systems as a new paradigm to enhance the quality of community (QOC). Social media plays an important role in creating, sharing, and exchanging information within a community. However, assistive technologies should be developed from human-centric and community-centric points of view to realize such information support. In this paper, we discuss the use of smart devices interlocked robot partners for interactive information support similar to concierge services in hotels. The interactive information support system is composed of two main subsystems, namely robot partners and informationally structured space servers. A robot partner performs communication and interaction with people through voice recognition and gesture recognition in addition to the use of touch interfaces. The informationally structured space server receives the measurement data of human motions and personal information containing human requests and preferences from the robot partner. Next, the informationally structured space server selects and recommends shops, restaurants, and sightseeing spots to the guests and visitors through utterance and display by the robot partner. First, we explain the concept of the informationally structured space to connect a person with sensory information and propose the overall architecture of the informationally structured space. Next, we explain how to provide information support using smart devices interlocked robot partners based on the informationally structured space. In addition, we describe several social experiments on interactive information support at hotels. Finally, we discuss the effectiveness of the proposed system and the future direction of community-centric systems.

In recent years, due to the shortage of labor caused by an aging population and low birth rate in Japan, various research on robots has been conducted. There are many industries in which applying autonomous mobile robots are important, for instance, the inspection system for waterway tunnels. Entering the waterway tunnel is a difficult task for humans, thus, the dark and damp environment of a waterway tunnel is unfriendly to humans both in the physical way and mental ways. To solve that problem, various vSLAM methods have been proposed in the past, including feature-based methods, direct methods, and RGB-D methods. However, bad illuminance condition and unclear texture of the ceiling and walls of a waterway tunnel brings difficulties to many vSLAM algorithms and frameworks. To improve the accuracy of vSLAM, various methods have been proposed, such as sensor fusion. In this paper, we proposed a system that contains an online tracking using Intel REALSENSE T265 and an offline map reconstruction based on opengl using the data gathered by Intel REALSENSE T265. For the experiments, we conducted two comparison experiments based on openvslam framework and our proposed system. The results show that our proposed system can successfully track the camera positions and construct a map.

In this paper, an unsupervised learning model of episodic memory is proposed. The proposed model, enhanced episodic memory adaptive resonance theory (EEM-ART), categorizes and encodes experiences of a robot to the environment and generates a cognitive map. EEM-ART consists of multilayer ART networks to extract novel events and encode spatio-temporal connection as episodes by incrementally generating cognitive neurons. The model connects episodes to construct a sensorimotor map for the robot to continuously perform path planning and goal navigation. Experimental results for a mobile robot indicate that EEM-ART can process multiple sensory sources for learning events and encoding episodes simultaneously. The model overcomes perceptual aliasing and robot localization by recalling the encoded episodes with a new anticipation function and generates sensorimotor map to connect episodes together to execute tasks continuously with little to no human intervention.

As the importance of lifelong learning and recurrent education has been emphasized in recent years, it can be argued that people must study for the rest of their lives. Many people, however, find it difficult to engage in and maintain learning behavior, and adequate learning support is required for people to participate in and maintain learning. We developed a learning support system based on the learner’s self-efficacy by the robot partner and explored its utility in this study to create an efficient learning support system by a robot partner. As a result, the proposed method’s effectiveness was demonstrated by the fact that the proposed learning support system increased the learner’s self-efficacy.

This paper proposes an ecological approach for object relationship extraction. First, we explain the ecological approach used to develop our previous works in the perception of a partner robot. Second, we developed a framework for finding the relation of objects based on scenarios of daily human activities. We represent the relationships among objects in a graph consisting of nodes and edges. Graph Convolutional Networks (GCN) is used to train the graph and perform semi-supervised classification. Third, we generate and prepare the datasets and then train the GCN. Finally, we present training visualization in two different scenarios. The proposed methods allow the system to identify the object based on its relationship. This approach is then used as a starting point to implement the ecological factors in elderly care robot applications.

In recent years, social robots are widely research due to the advancement of electronics hardware and deep learning algorithms breakthrough. Hand gesture recognition playing an important role in human-robot interaction. However, to deploy state-of-art of deep learning methods for hand gesture recognition, researchers have to associate all hand gestures with their respective meanings (label). Re-train the deep learning model is required if a new gesture added for recognition. In addition, the meaning of hand gestures often depending on the environmental conditions. In this paper, we propose a framework that learns and recognizes hand gesture meaning based on surrounding objects. The proposed method consists of two sub frameworks: i) A deep learning model, Mask RCNN to detect and extract features of objects and hand gestures; ii) An incremental learning model, Declarative Memory Recurrent Neural Model to incrementally learn hand gestures meaning based on surrounding objects. The effectiveness of our proposed method is validated through several experiments.

Recently, various types of robot partners aiming at social implementation have been developed and researched, but they have not yet been popularized. In the case of communication robots, it has been reported that it is easier and more comfortable for humans to communicate with multiple robots. However, multirobots require large installation space; the role or character of each robot is not clear, and it is difficult for users to design utterance and action contents. Therefore, we propose a new design methodology of the body-sharing multi-robot system and develop a kangaroo-based robot that contains two physically interconnected objects as one of the models of multiple communication robots. Next, we show the evaluation of the proposed robot in terms of impression and discuss the possibility of social implementation of the developed body sharing multirobot system.

Recently, as the rapid progress of information and communication technology, robot technology, and artificial intelligence, we have become to build a higher level of safe, comfortable, and smart society coexisting with advanced technology. Methamphetamine addiction has become a major human social problem in the world. Traditional approaches of detecting methamphetamine through hair, skin, urine, and blood fluid are financially inefficient, time-consuming, and in some cases too complicated. Providing reliable and trustworthy detection with the highest precision and accuracy is of a challenging task. This paper proposes ensemble learning using a soft voting approach to improve the accuracy of detection. First, we trained five individual classifiers, namely adaptive neuro-fuzzy inference system (ANFIS), random forest, multilayer perceptron (MLP), k-nearest neighbor (k-NN), and support vector machine (SVM) on the same urine dataset. We then created new ensemble learning using the soft voting approach by averaging the probability of individual ANFIS, random forest, MLP, k-NN, and SVM. Firefly algorithm for weight optimization is used to strengthen individual classifiers to form an ensemble and increase the prediction accuracy. Our proposed ensemble produces an accuracy value of 100% compared to the individual classifiers mentioned above.

This paper presents a method with dynamic density topological structure generation for low-cost real-time vertical ladder detection from 3D point cloud data. Dynamic Density Growing Neural Gas (DD-GNG) is proposed to generate a dynamic density of the topological structure. The density of the structure and the number of nodes will be increased in the targeted object area. Feature extraction model is required to classify suspected objects for being processed in the next time process. After that, rungs of the vertical ladder is processed using an inlier-outlier method. Thus, the ladder detection model represents the ladder with a set of nodes and edges. Next, affordance detection is processed for detecting the feasible grasped location. To validate the effectiveness of the proposed method, a series of experiments are conducted on a 4-legged robot with a non-GPU board for real-time vertical ladder detection and climbing to validate the effectiveness of the proposed method. Results show that our proposed method able to detect and track the ladder structure in real-time with a much lower computational cost. The affordance of the ladder provides safety information for robot grasping.

Biological information inspires the advancement of a navigational mechanism for autonomous robots to help people explore and map real-world environments. However, the robot’s ability to constantly acquire environmental information in real-world, dynamic environments has remained a challenge for many years. In this paper, we propose a self-organizing adaptive recurrent incremental network that models human episodic memory to learn spatiotemporal representations from novel sensory data. The proposed method termed as SOARIN consists of two main learning process that is active learning and episodic memory playback. For active learning (robot exploration), SOARIN quickly learns and adapts incoming novel sensory data as episodic neurons via competitive Hebbian Learning. Episodic neurons are connecting with each other and gradually forms a spatial map that can be used for robot localization. Episodic memory playback is triggered whenever the robot is in an inactive mode (charging or hibernating). During playback, SOARIN gradually integrates knowledge and experience into more consolidate spatial map structures that can overcome the catastrophic forgetting. The proposed method is analyzed and evaluated in term of map learning and localization through a series of real robot experiments in real-world indoor environments.

This paper presents a new integration model for on-line topological map building with environment slow features detection. The proposed model is formed by the integration of Bayesian Adaptive Resonance Associative Memory (BARAM) and Incremental Slow Feature Analysis (IncSFA). IncSFA incrementally extracts slowly varying features from a rapidly changing input signal. These slow features will be fed to BARAM for environment learning to build a topological map. The explored environment is represented as a set of neurons (nodes) and edges that connecting all nodes. Each neuron represents a distinct place and edges store robot traverse information that leads the robot to travel from one node to another. The proposed model is an unsupervised learning technique that does not require any prior knowledge of what an environment is supposed to be for ease of implementation. The effectiveness of our proposed method is validated by several standardized benchmark datasets.

In this paper, we present a novel biologically inspired evolving neural oscillator for quadruped robot locomotion to minimize constraints during the locomotion process. The proposed sensory-motor coordination model is formed by the interconnection between motor and sensory neurons. The model utilizes Bacterial Programming to reconstruct the number of joints and neurons in each joint based on environmental conditions. Bacterial Programming is inspired by the evolutionary process of bacteria that includes bacterial mutation and gene transfer process. In this system, either the number of joints, the number of neurons, or the interconnection structure are changing dynamically depending on the sensory information from sensors equipped on the robot. The proposed model is simulated in computer for realizing the optimization process and the optimized structure is then applied to a real quadruped robot for locomotion process. The optimizing process is based on tree structure optimization to simplify the sensory-motor interconnection structure. The proposed model was validated by series of real robot experiments in different environmental conditions.

In this article, we propose a new approach for mobile robot localization and hybrid map building simultaneously without using any odometry hardware system. The proposed method termed as Genetic Bayesian ARAM comprises two main components: (1) steady-state genetic algorithm (SSGA) for self-localization and occupancy grid map building and (2) Bayesian Adaptive Resonance Associative Memory (ARAM) for online topological map building. The model of the explored environment is formed as a hybrid representation, both topological and grid based, and it is incrementally constructed during the exploration process. During occupancy map building, the robot-estimated self-position is updated by SSGA. At the same time, the robot-estimated self-position is transmitted to Bayesian ARAM for topological map building and localization. The effectiveness of our proposed approach is validated by a number of standardized benchmark datasets and real experimental results carried on the mobile robot. Benchmark datasets are used to verify the proposed method capable of generating topological map in different environment conditions. Real robot experiment to verify the proposed method can be implemented in real world.