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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 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.

Personal mobility vehicles (PMV) with the autonomous driving system is required for supporting movement-limited persons. To realize their safe movement, researchers extract pedestrian features from images without considering pedestrian characteristics. However, before these extractions, it is also important to estimate which person should be paid attention and assigning priority order by the degree of danger for PMV’s collision avoidance. In this paper, we propose a deep neural model for pedestrian detection with danger estimation. Finally, we show some experimental results and discuss parameters that are important for pedestrian danger estimation for each experimental scenario.

Falls are a major health issue, particularly among the elderly. Increasing fall events require high service quality and dedicated medical treatment which is an economic burden. In the lack of appropriate care and support, serious injuries caused by fall will cost lives. Therefore, tracking systems with fall detection capabilities are required. Static-view sensors with machine learning techniques for human fall detection have been widely studied and achieved significant results. However, these systems unable to monitor a person if he or she is out of viewing angle which greatly impedes its performance. Mobile robots are an alternative for keeping the person in sight. However, existing mobile robots are unable to operate for a long time due to battery issues and movement constraints in complex environments. In this paper, we proposed a lightweight deep learning vision-based model for human fall detection with an assistive robot to provide assistance when a fall happens. The proposed detection system requires less computational power which can be implemented in a low-cost 2D camera and GPU board for real-time monitoring. The assistive robot equipped with various sensors that can perform SLAM, obstacle avoidance and navigation autonomously. Our proposed system integrates these two sub-systems to compensate for the weakness of each other to constitute a system that robust, adaptable, and high performance. The proposed method has been validated through a series of experiments.

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.

Due to many applications such as video surveillance, human machines interaction and video recovery, human actions recognition was a significant topic in computer vision. This paper proposes a self-organizing recurrent incremental network (SORIN) for human action recognition using human skeleton information. The proposed method models human working memory and episodic memory and comprises two layers of adaptive recurrent Growing-When-Required (ar-GWR) network that connected hierarchically. The working memory layer continually learns incoming perception information and encodes the learned knowledge as neurons. Similarly, the episodic memory layer further learns the spatiotemporal relationship of neurons from working memory as episode neurons to realize human actions incrementally. The proposed method integrates with OpenPose framework for human skeleton action recognition and it is validated through a series of experiments.

Biologically inspired computational techniques play a crucial role in robotic cognition. Artificial learning agents and robots that interact in complex environments must constantly acquire and refine knowledge over long periods of time. In this paper, we propose a novel recurrent neural architecture that mimics humans’ declarative memory system for continuously generating a cognitive map during robot navigation. The proposed method termed as Declarative Memory Adaptive Recurrent Model (DM-ARM), and consists of three hierarchical memory courses: (i) Working Memory, (ii) Episodic Memory and (iii) Semantic Memory layer. Each memory layer comprises a self-organizing adaptive recurrent incremental network (SOARIN) with a different learning task respectively. The Working Memory layer quickly clusters sensory information while the Episodic Memory layer learns fine-grained spatiotemporal relationships of clusters (temporal encoding). Both the memory layer learning is in an unsupervised manner. The Semantic Memory layer utilizes task-relevant cues to adjust the level of architectural flexibility and generate a semantic map that contains more compact episodic representations. The effectiveness of the proposed recurrent neural architecture is evaluated through a series of experiments. We implemented and validated our proposed work on the tasks of robot navigation.

Human actions can be realized by observing the trajectories of skeleton joints. In this paper, we propose an unsupervised episodic memory learning model for skeleton based action learning and recognition. The proposed model, Multi-channel Episodic Memory Adaptive Resonance Theory (McEM-ART), consists of three layers: short term memory, working memory and Episodic memory. The short term memory layer is formed by multiple ART networks to obtain sensory data and cluster them into neurons in working memory layer. Instead of obtaining the whole skeleton as an input, we divide the human skeleton into three parts, upper part body, main body and lower part body. Each of them is then feed to McEM-ART short term memory layer for learning. Episodic memory layer extracts novel events and encodes spatio-temporal connection between them as episodes by generating cognitive neurons incrementally for action recognition. Comparing with previous works, McEM-ART further integrates a novel memory anticipation functions for encoding crucial events and episodes and recalling them using partial and inexact cues. Experimental results demonstrate that McEM-ART is capable of clustering human skeleton data into event neurons, encoding sequence of activation events as episode neurons for action recalling and recognition.

In this paper, an episodic memory learning framework is proposed for categorizing and encoding sensory information that acquired from a robot for environment adaptation and sensorimotor map building. The proposed learning model termed as Incremental Episodic Memory Adaptive Resonance Theory (In-EMART), consists two layers of ART networks which used to detect novel event encountered by the robot and learn the spatio-temporal relationship by creating neurons incrementally. A set of connected episodes forms a sensorimotor map that can be used for path planning and goal navigation autonomously. The experimental results for a mobile robot show that: (i) In-EMART can learn sensory data in real time which is important for robot implementation; (ii) the model solves the perceptual aliasing issue by recalling the connected episode neurons; (iii) compared with previous works, the proposed method further generates a sensorimotor map for connecting episodes together to navigate from one place to another continuously.

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.

Graphical abstractDisplay Omitted HighlightsMBARAM enables topological map building with little or no human intervention.Does not require high-level cognitive and prior knowledge to function in a natural environment.Multiple sensory sources are processes simultaneously crucial for real-world robot navigation.Results show capability of generating topological map online used for localization. In this paper, a new network is proposed for automated recognition and classification of the environment information into regions, or nodes. Information is utilized in learning the topological map of an environment. The architecture is based upon a multi-channel Adaptive Resonance Associative Memory (ARAM) that comprises of two layers, input and memory. The input layer is formed using the Multiple Bayesian Adaptive Resonance Theory, which collects sensory data and incrementally clusters the obtained information into a set of nodes. In the memory layer, the clustered information is used as a topological map, where nodes are connected with edges. Nodes in the topological map represent regions of the environment and stores the robot location, while edges connect nodes and stores the robot orientation or direction. The proposed method, a Multi-channel Bayesian Adaptive Resonance Associative Memory (MBARAM) is validated using a number of benchmark datasets. Experimental results indicate that MBARAM is capable of generating topological map online and the map can be used for localization.

Topological structure learning methods are expected for the field of data mining for extracting multiscale topological structures from an unknown dataset. In this paper, we introduce the unsupervised neural network method for topological structure learning method from point clouds for map building. We propose Batch Learning GNG (BL-GNG) in order to improve the learning convergence. BL-GNG uses an objective function based on Fuzzy C-means for improving the learning convergence. Finally, we conduct on several experiments for evaluating our proposed method by comparing to other hierarchical approaches, and discuss the effectiveness of our proposed method.

This paper presents a novel combination method for on-line topological map building and dynamic path planning. The proposed method consists of two main components: Bayesian Adaptive Resonance Associative Memory (Bayesian ARAM) and forward-backward propagation path planner. Bayesian ARAM incrementally clusters sensory information and generates topological map. The explored environment is described as a group of neurons (nodes) and edges. Each neuron (nodes) represents a distinct place and it is defined as multi-dimensional Gaussian distribution which does not require any prior knowledge of what a place is supposed to be to make it works in natural environment. The topological map is incrementally generated by Bayesian ARAM. The forward-backward propagation path planner consists of two process: forward propagation determines the possible path while backward propagation with neuron pruning eliminates inefficient neurons and determines the optimum pathway from current location to target location based on the generated map information. The effectiveness of our proposed method is validated by several standardized benchmark datasets.

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.