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The pursuit of higher levels of autonomy and versatility in robotics is arguably led by two main factors. First, as we push robots out of the labs and production lines, it becomes increasingly challenging to design for all possible scenarios that a particular robot might encounter. Second, the cost of designing, manufacturing, and maintaining such systems becomes prohibitive.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

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Recent developments in robotics and information technology have been applied to robots to improve their perception, cognition, learning, and control abilities to enhance the performance [items 1)–6) in the Appendix]. A challenging issue on robotics research is to make the robots working around with human more flexibly and safely. To address the challenge, this special issue focuses on the latest advances

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

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In this paper, we propose a Transformer-based RGB-D egocentric action recognition framework, called Trear. It consists of two modules, inter-frame attention encoder and mutual-attentional fusion block. Instead of using optical flow or recurrent units, we adopt self-attention mechanism to model the temporal structure of the data from different modalities. Input frames are cropped randomly to mitigate

Eye activity-based within-task cognitive load measurement (CLM) is currently not feasible in everyday situations. One important issue to be addressed to move such CLM beyond controlled laboratory environments is determining practical methods for mitigating the pupillary light reflex (PLR) effect in CLM. In this paper, four approaches to dealing with the PLR effect within a modified verbal digit span

In the reinforcement learning (RL) system, one important issue is the tradeoff problem between exploration and exploitation. In this paper, we studied this dilemma and proposed a new approach to solving this problem by multiple-attribute decision making (MADM). The applicability of the proposed method is extended by transfer learning. The method decomposes a task into several subtasks and uses the

The scalp electroencephalogram (EEG)-based epileptic seizure/nonseizure detection has been comprehensively studied, and fruitful achievements have been reported in the past. Yet, few investigations have been paid to the preictal stage detection, which is practically more crucial to epileptics in taking precautions before seizure onset. In this article, a novel epileptic preictal state classification

Humans are inherently social beings that benefit from their perceptional capability to embody another point of view, typically referred to as perspective taking. Perspective taking is an essential feature in our daily interactions and is pivotal for human development. However, much remains unknown about the precise mechanisms that underlie perspective taking. Here, we show that formalizing perspective

For decades, a variety of task-based functional MRI (tfMRI) data analysis approaches have been developed, including the general linear model (GLM), sparse representations, and independent component analysis (ICA). However, these methods are mainly shallow models and are limited in faithfully modeling the complex, diverse, and concurrent spatial–temporal functional brain activities. Recently, recurrent

Data-driven deep learning solutions with gradient-based neural architecture, have proven useful in overcoming some limitations of traditional signal processing techniques. However, a large number of reverberant–anechoic training utterance pairs covering as many environmental conditions as possible is required to achieve robust dereverberation performance in unseen testing conditions. In this article

The concept of direction is critical for a robot to establish its abilities in spatial perception. This article addresses the issue of how a robot developmentally forms the concept of direction. We propose a novel framework, based on the related mechanisms of humans, in which motion cues are employed instead of other commonly used sensing means like vision or audition. Using motion cues is actually

Both motor and cognitive function rehabilitation benefits can be improved significantly by patients’ active participation. To this goal, an attention enhancement system based on the brain–computer interface (BCI) and audiovisual feedback is proposed. First, an interactive position-tracking riding game is designed to increase the training challenge and neural engagement. Subjects were asked to drive

Neuromodulation and neuroplasticity work together to help organisms learn to cope in their environment. Experiments demonstrate that simple forms of neuromodulation aid in the learning process. In those studies, neuromodulation was used as a multiplier to scale the learning rate. However, more complex interactions have not been investigated. Our contributions are twofold: 1) we evolve a subnetwork

Video games are a widespread leisure activity and essential for a substantial field of research. In several kinds of research, video games show positive effects on cognition. Video games’ ability to change the brain in a way that improves cognition is already evident in the research world. The underlying brain dynamics assessed by coherence (Coh) and partial-directed coherence (PDC) can shed light

Prior research has used reinforcement-learning models to investigate human decisions in choice games. However, research has not investigated how reinforcement-learning models expectancy valence learning (EVL) and prospect valence learning (PVL) would explain human decisions in applied judgment games where people face a collective risk social dilemma (CRSD) against societal problems such as climate

Presents the table of contents for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

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Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.

This article presents a skill learning-based hierarchical control strategy for human–robot cooperative manipulation, which constitutes a novel learning-control system. The high-level learning strategy aims to learn the motor skills from human demonstrations by fusion with dynamic motion primitives (DMPs) and the Gaussian mixture model (GMM). The lower level control strategy guarantees the compliance

Infant vocal babbling strongly relies on jaw oscillations, especially at the stage of canonical babbling, which underlies the syllabic structure of world languages. In this paper, we propose, model and analyze an hypothesis to explain this predominance of the jaw in early babbling. This hypothesis states that general stochastic optimization principles, when applied to learning sensorimotor control

Gene regulatory network (GRN)-based morphogenetic models have recently gained an increasing attention. However, the relationship between microscopic properties of intracellular GRNs and macroscopic properties of morphogenetic systems has not been fully understood yet. Here, we propose a theoretical morphogenetic model representing an aggregation of cells, and reveal the relationship between criticality

Children exploit their morphosyntactic knowledge in order to infer the meanings of words. A recent behavioral study has reported developmental changes in word learning from three to five years of age, with respect to a child’s native language. To understand the computational basis of this phenomenon, we propose a model based on a hidden Markov model (HMM). The HMM acquires syntactic categories of given

Image-based visual servoing (IBVS) achieves precise positioning and motion control for a relatively stationary target by visual feedback, but problems persist with convergence and stability. Appropriate servoing gains for the IBVS are critical to the convergence and stability, but this control gain is heuristically a constant for most IBVS applications. This paper proposes an integrated method that

A common learning task for a spiking neuron is to map a spatio–temporal input pattern to a target output spike train. There is no prescribed method for selection of the target output spike train. However, the precise spiking pattern of the target output spike train (output encoding) can affect the learning performance of the spiking neuron. Therefore, systematic methods of finding the optimum spiking

Reminiscence is a lifelong activity that happens throughout our lifespan. While memories can serve as topics in people’s everyday conversations, recalling the past can also help us build self-esteem and increase our level of happiness. In this paper, we aim to develop a robot companion that helps people to recollect their memories from personal photographs. We focus on how a robot can associate concepts

Since the human brain functional mechanism has been enabled for investigation by the functional magnetic resonance imaging (fMRI) technology, simultaneous modeling of both the spatial and temporal patterns of brain functional networks from 4-D fMRI data has been a fundamental but still challenging research topic for neuroimaging and medical image analysis fields. Currently, general linear model (GLM)

Today, fingerprint recognition technology has aroused wide attention in the society, especially in the application of identity authentication with a smartphone as a carrier. However, the disadvantage of these devices is that the identification sensors are vulnerable to spoofing attacks from artificial replicas made from clay, gelatin, silicon, etc. To resolve it, a feasible anti-deception countermeasure

$Q$ -learning is a generic approach that uses a finite discrete state and an action domain to estimate action values using tabular or function approximation methods. An intelligent agent eventually learns policies from continuous sensory inputs and encodes these environmental inputs onto a discrete state space. The application of $Q$ -learning in a continuous state/action domain is the subject of many

Electroencephalography (EEG) is another method for performing person identification (PI). Due to the nature of the EEG signals, EEG-based PI is typically done while a person is performing a mental task such as motor control. However, few studies used EEG-based PI while the person is in different mental states (affective EEG). The aim of this paper is to improve the performance of affective EEG-based

Experimental observations from neuroscience have suggested that the cognitive process of human brain is realized as probabilistic reasoning and further modeled as Bayesian inference. However, it remains unclear how Bayesian inference could be implemented by network of neurons in the brain. Here a novel implementation of neural circuit, named the sampling-tree model, is proposed to fulfill this aim

Robust multiperson tracking requires the correct associations of online detection responses with existing trajectories. In this paper, we propose to integrate multiple cues to resolve the ambiguities in data association for multiperson tracking. Unlike most existing algorithms which integrate multiple cues in the same manner for different scenes, we learn scene-specific parameters to integrate multiple

In this article, a decentralized planning scheme is proposed to determine simultaneously communication and motion strategies for a team of mobile robots. These robots accomplish a collection of target visiting tasks in a complex environment with optimal energy consumption and guaranteed end-to-end connectivity. Information generated during the team deployment is transmitted to an operation center via

Memristive neuromorphic systems are emerging potential hardware platforms to implement artificial neural networks. Combining features of memristive neuromorphic systems with associative memory, this article proposes an associative-memory-based reconfigurable memristive neuromorphic system. In the proposed system, there are two neural networks: 1) the neural network for computing acceleration and 2)

Sentiment analysis (SA) is the process of evaluating human emotions, opinions, and reviews expressed in text to detect the writer’s mental outlook toward a particular event, topic, product, service, etc., and assign a relevant sentiment. In SA, to highlight the correct words which contribute toward sentiment cognition is very difficult. Simulating this task of shortlisting of words by human observers

The existing Devanagari-script-input-based P300 speller (DS-P3S) performs better mostly with 3–15 trials. This leads to poor information transfer rate (ITR) and a major concern in its real-time adaptation. In DS-P3S, the display paradigm is a matrix of $8\times 8$ size which has 28 more characters than the $6\times 6$ English paradigm. The increased number of characters leads to user-related issues

Multiple autonomous underwater vehicles (AUVs) are popular for challenging submarine missions. In this article, we focus on the multi-AUV path planning for a common class of missions that need to traverse lots of mission targets in large and complex environments. Given that AUVs are often launched from a movable surface vehicle, e.g., a ship, a multi-AUV target traveling problem is formulated with

Time-varying dynamic system contaminated by cognitive noises is universal in the fields of engineering and science. In this article, a circadian rhythms learning network (CRLN) is proposed and investigated for disposing the noise disturbed time-varying dynamic system. To do so, a vector-error function is first defined. Second, a neural dynamic model is formulated. Third, a co-state matrix is integrated

In this article, we describe an approach for the problem of fall detection among the several other everyday activities in indoor environment, using three uncalibrated fisheye cameras. The proposed methodology requires the input segmented silhouettes from the three simultaneously acquired frames, and it is capable of detecting fall events in every location of the imaged environment. The presented algorithm

It has been a long-term dream of roboticist to create a robot that has similar behavior and appearance to human. With highly redundant joints and actuators, the musculoskeletal system of human can fulfill movement and operation with high precision, flexibility, and reliability. However, the redundancy of the musculoskeletal system also brings great difficulties to the control and fabrication of a human-like

Perceptual-ability informally refers to the ability of a person to recognize a stimulus. This article deals with color perceptual-ability measurement of subjects using brain response to basic color (red, green, and blue) stimuli. It also attempts to determine the subjective ability to recognize the base colors in the presence of noise tolerance of the base colors, referred to as recognition tolerance

In multiagent cooperative systems with value-based reinforcement learning, agents learn how to complete the task by an optimal policy learned through value-policy improvement iterations. But how to design a policy that avoids cooperation dilemmas and comes to a common consensus between agents is an important issue. A method that improves the coordination ability of agents in cooperative systems by

Napping in the workplace has become popular. Knowing how to nap for brain benefits is important. We designed a nap experiment to investigate how napping after different sleep stages impacts procedural memory and sleepiness. In total, 45 nonhabitual nappers were randomly assigned to the Wake group (no napping), N2 group (napping and being woken after enough N2 sleep), and slow-wave sleep (SWS) group

We present two related but different cross-situational and crossmodal models of incremental word learning. Model 1 is a Bayesian approach for co-learning object-word mappings and referential intention which allows for incremental learning from only a few situations where the display of referents to the learning system is systematically varied. We demonstrate the robustness of the model with respect