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Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

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Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

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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

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

Human facial and bodily expressions play a crucial role in human–human interaction to convey the communicator’s feelings. Being echoed by the influence of human social behavior, recent studies in human–robot interaction (HRI) have investigated how to generate emotional behaviors for social robots. Emotional behaviors can enhance user engagement, allowing the user to interact with robots in a transparent

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Humans excel at learning from other humans [item 1) in the Appendix). Language facilitates such learning and plays a crucial role. On the one hand, it coordinates our interactions and cooperative behavior [item 2) in the Appendix). On the other hand, language and communication allow to directly incorporate novel knowledge gathered from social interaction or from reading [item 3) in the Appendix). It

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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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

This article explores an alternative design philosophy of compliance control for safe and friendly human–robot interaction. Conventional approaches are based on the Voigt model with spring and damper connected in parallel. The theoretical and experimental results of disciplines other than robotics (e.g., mechanics of materials and elastoplastic mechanics) show that the Maxwell model has the merit of

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

A prediction error negativity (PEN) can be observed in the human electroencephalogram when there is a mismatch between the predicted and the perceived changes in the environment. Our previous study using a virtual object selection task demonstrated an impact of the level of avatar realism on the PEN, reflecting a mismatch between visual and proprioceptive feedback about the object selection. To investigate

Deception is a human behavior and its cognitive process and mechanism involve complex neuronal activities of the brain. In this article, we develop a simple and feasible concealed information test (CIT) method which is based on the audio–visual event-related potentials (ERPs) and its spatial and temporal features. The main purpose of this article is to extend a pattern recognition method with functional

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This paper proposes a secondary reactive collision avoidance system for microclass of robots based on a novel approach known as the furcated luminance-difference processing (FLDP) inspired by the lobula giant movement detector, a wide-field visual neuron located in the lobula layer of a locust nervous system. This paper addresses some of the major collision avoidance challenges: obstacle proximity

Recent evidence appears to show a close connection between explanation and belief revision, specifically, the revision of graded beliefs. Insofar as this is also evidence of violations of Bayesian norms of reasoning, the question arises whether we are facing a new bias here, on a par with previously discovered biases in probabilistic reasoning. We consider an apparently successful attempt by Costello

In order to extend promising robot applications in human daily lives, robots need to perform dextrous manipulation tasks, particularly for a mobile dual-arm robot. This paper propose a novel control strategy, which consists of a first trial process and a learning phase, to enable a mobile dual-arm robot to complete a grasp-and-place task which can be decomposed into movement sequences, such as reaching

With the massive deployment of distributed video surveillance systems, the automatic detection of abnormal events in video streams has become an urgent need. An abnormal event can be considered as a deviation from the regular scene; however, the distribution of normal and abnormal events is severely imbalanced, since the abnormal events do not frequently occur. To make use of a large number of video

Nerve injury after stroke leads to disorders of locomotion and a declining balance function, which increases the risk of falling. Restriction of pelvic motions can hinder successful rehabilitation, hence a robotic-assisted rehabilitation trainer (RART) is proposed to assist patients in controlling the pelvic motions via force field. The mechanical design, kinetic framework, and the intention-based

A deep feature-based saliency model (DeepFeat) is developed to leverage understanding of the prediction of human fixations. Conventional saliency models often predict the human visual attention relying on few image cues. Although such models predict fixations on a variety of image complexities, their approaches are limited to the incorporated features. In this paper, we aim to utilize the deep features

Rapid advancement of machine learning makes it possible to consider large amounts of data to learn from. Learning agents may get data ranging on real intervals directly from the environment they interact with, in a process usually time expensive. To improve learning and manage these data, approximated models and memory mechanisms are adopted. In most of the implementations of reinforcement learning

Zero-shot classification is a hot topic in computer vision and pattern recognition. Most zero-shot classification methods are based on the intermediate level representation of attributes to achieve knowledge transfer from the training classes to the unseen test classes. Recently, multitask learning (MTL) has been shown as one of state-of-the-art approaches for attribute learning and zero-shot classification

This paper addresses the topic of semantic world modeling by conjoining probabilistic reasoning and object anchoring. The proposed approach uses a so-called bottom-up object anchoring method that relies on rich continuous attribute values measured from perceptual sensor data. A novel anchoring matching function learns to maintain object entities in space and time and is validated using a large set

Deep-neural-networks-based online visual tracking methods have achieved state-of-the-art results. One of the core components of these methods is the memory pool, in which a number of samples consisting of image patches and the corresponding labels are stored to update the online tracking network. Hence, the mechanism of updating the stored samples determines the performance of the tracking method.

Today users can interact with popular virtual assistants such as Siri to accomplish their tasks on a digital environment. In these systems, links between natural language requests and their concrete realizations are specified at the conception phase. A more adaptive approach would be to allow the user to provide natural language instructions or demonstrations when a task is unknown by the assistant

Dodging emergent dangers is an innate cognitive ability for animals, which helps them to survive in the natural environment. The retina-superior colliculus (SC)-pulvinar–amygdala–periaqueductal gray pathway is responsible for the visual fear responses, and it is able to quickly detect the looming obstacles for innate dodging. Inspired by the mechanism of the visual fear responses pathway, we propose

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Postural synergies allow a rich set of hand configurations to be represented in lower dimension space compared to the original joint space. In our previous works, we have shown that this can be extended to trajectories thanks to the multivariate functional principal component analysis, obtaining a set of basis functions able to represent grasping movements learned from human demonstration. In this

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

Effective auto-diagnosis assistants can benefit our healthcare system in various aspects, such as, saving labor cost, sharing knowledge among the crowd, and timely supporting the patients. However, the existing auto-diagnosis models are ineffective due to issues caused by information island, poor information coding, and inefficient informative retrieval. To address these issues, this article presents

The employment of personal robots or service robots has aroused much interest in recent years with an amazing growth of robotics in different domains. Although sophisticated humanoid robots have been developed, much more effort is needed for improving their cognitive capabilities. Interactions with humans and/or with other agents are still limited and not considered satisfactory. So, the way we store

In this article, we present a control architecture for a robotic manipulator finally aimed at helping people with severe motion disabilities in performing daily life operations, such as manipulating objects or drinking. The proposed solution allows the user to focus the attention only on the operational tasks, while all the safety-related issues are automatically handled by the developed control architecture

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Internal models (IMs) can represent relations between sensors and actuators in natural and artificial agents. In autonomous robots, the adaptation of IMs and the adaptation of the behavior are interdependent processes which have been studied under paradigms for self-organization of behavior such as homeokinesis. We compare the effect of various types of IMs on the generation of behavior in order to

We propose an anomaly detection approach by learning a generative model of moving pedestrians to guarantee public safety. To resolve the existing challenges of anomaly detection in complicated definitions, complex backgrounds, and local occurrence, a weighted convolutional autoencoder-long short-term memory network is proposed to reconstruct raw data and their corresponding optical flow and then perform