Robot Interaction Research Articles

Design Techniques for the Optimal Creation of a Robot for Interaction with Children with Autism Spectrum Disorder

Educational robotics is a sector that is being integrated into classrooms to achieve innovative and effective learning in the early stages of children’s education. However, it is not only applied in education but has great importance in its use with children with special needs to improve their quality of life. The convenience of robots applied to interaction with children with autism spectrum disorder (ASD) has been widely demonstrated. In this work, a study is carried out on what design patterns a robot focused on children with autism should have. It is necessary to make known the characteristics of this and, from there, how to apply these concepts to the effective design of a device. A robot, TEA-2, is proposed that encompasses all these guidelines in a single robot.

Human–robot interaction through joint robot planning with large language models

Human–robot interaction through joint robot planning with large language models

Versatile adhesive skin enhances robotic interactions with the environment.

Electronic skins endow robots with sensory functions but often lack the multifunctionality of natural skin, such as switchable adhesion. Current smart adhesives based on elastomers have limited adhesion tunability, which hinders their effective use for both carrying heavy loads and performing dexterous manipulations. Here, we report a versatile, one-size-fits-all robotic adhesive skin using shape memory polymers with tunable rubber-to-glass phase transitions. The adhesion strength of our adhesive skin can be changed from minimal (~1 kilopascal) for sensing and handling ultralightweight objects to ultrastrong (>1 megapascal) for picking up and lifting heavy objects. Our versatile adhesive skin is expected to greatly enhance the ability of intelligent robots to interact with their environment.

Multi-Modal Social Robot Behavioural Alignment and Learning Outcomes in Mediated Child-Robot Interactions.

With the increasing application of robots in human-centred environments, there is increasing motivation for incorporating some degree of human-like social competences. Fields such as psychology and cognitive science not only provide guidance on the types of behaviour that could and should be exhibited by the robots, they may also indicate the manner in which these behaviours can be achieved. The domain of social child-robot interaction (sCRI) provides a number of challenges and opportunities in this regard; the application to an educational context allows child-learning outcomes to be characterised as a result of robot social behaviours. One such social behaviour that is readily (and unconsciously) used by humans is behavioural alignment, in which the behaviours expressed by one person adapts to that of their interaction partner, and vice versa. In this paper, the role that robot non-verbal behavioural alignment for their interaction partner can play in the facilitation of learning outcomes for the child is examined. This behavioural alignment is facilitated by a human memory-inspired learning algorithm that adapts in real-time over the course of an interaction. A large touchscreen is employed as a mediating device between a child and a robot. Collaborative sCRI is emphasised, with the touchscreen providing a common set of interaction affordances for both child and robot. The results show that an adaptive robot is capable of engaging in behavioural alignment, and indicate that this leads to greater learning gains for the children. This study demonstrates the specific contribution that behavioural alignment makes in improving learning outcomes for children when employed by social robot interaction partners in educational contexts.

Terrain Traversability via Sensed Data for Robots Operating Inside Heterogeneous, Highly Unstructured Spaces.

This paper presents a comprehensive approach to evaluating the ability of multi-legged robots to traverse confined and geometrically complex unstructured environments. The proposed approach utilizes advanced point cloud processing techniques integrating voxel-filtered cloud, boundary and mesh generation, and dynamic traversability analysis to enhance the robot's terrain perception and navigation. The proposed framework was validated through rigorous simulation and experimental testing with humanoid robots, showcasing the potential of the proposed approach for use in applications/environments characterized by complex environmental features (navigation inside collapsed buildings). The results demonstrate that the proposed framework provides the robot with an enhanced capability to perceive and interpret its environment and adapt to dynamic environment changes. This paper contributes to the advancement of robotic navigation and path-planning systems by providing a scalable and efficient framework for environment analysis. The integration of various point cloud processing techniques into a single architecture not only improves computational efficiency but also enhances the robot's interaction with its environment, making it more capable of operating in complex, hazardous, unstructured settings.

Vocal Communication Between Cobots and Humans to Enhance Productivity and Safety: Review and Discussion

This paper explores strategies for fostering efficient vocal communication and collaboration between human workers and collaborative robots (cobots) in assembly processes. Vocal communication enables the division of attention of the worker, as it frees their visual attention and the worker’s hands, dedicated to the task at hand. Speech generation and speech recognition are pre-requisites for effective vocal communication. This study focuses on cobot assistive tasks, where the human is in charge of the work and performs the main tasks while the cobot assists the worker in various peripheral jobs, such as bringing tools, parts, or materials, and returning them or disposing of them, or screwing or packaging the products. A nuanced understanding is necessary for optimizing human–robot interactions and enhancing overall productivity and safety. Through a comprehensive review of the relevant literature and an illustrative example with worked scenarios, this manuscript identifies key factors influencing successful vocal communication and proposes practical strategies for implementation.

Human–Robot Interactions: A Pilot Study of Psychoaffective and Cognitive Factors to Boost the Acceptance and Usability of Assistive Wearable Devices

Robotic technology to assist rehabilitation provides practical advantages compared with traditional rehabilitation treatments, but its efficacy is still disputed. This controversial effectiveness is due to different factors, including a lack of guidelines to adapt devices to users’ individual needs. These needs include the specific clinical conditions of people with disabilities, as well as their psychological and cognitive profiles. This pilot study aims to investigate the relationships between psychological, cognitive, and robot-related factors playing a role in human–robot interaction to promote a human-centric approach in robotic rehabilitation. Ten able-bodied volunteers were assessed for their anxiety, experienced workload, cognitive reserve, and perceived exoskeleton usability before and after a task with a lower-limb exoskeleton (i.e., 10 m path walking for 10 trials). Pre-trial anxiety levels were higher than post-trial ones (p < 0.01). While trait anxiety levels were predictive of the experienced effort (Adjusted-r2 = 0.43, p = 0.02), the state anxiety score was predictive of the perceived overall workload (Adjusted-r2 = 0.45, p = 0.02). High–average cognitive reserve scores were predictive of the perception of exoskeleton usability (Adjusted-r2 = 0.45, p = 0.02). A negative correlation emerged between the workload and the perception of personal identification with the exoskeleton (r = −0.67, p-value = 0.03). This study provides preliminary evidence of the impact of cognitive and psychoaffective factors on the perception of workload and overall device appreciation in exoskeleton training. It also suggests pragmatic measures such as familiarization time to reduce anxiety and end-user selection based on cognitive profiles. These assessments may provide guidance on the personalization of training.

A Symmetrical Leech-Inspired Soft Crawling Robot Based on Gesture Control.

This paper presents a novel soft crawling robot controlled by gesture recognition, aimed at enhancing the operability and adaptability of soft robots through natural human-computer interactions. The Leap Motion sensor is employed to capture hand gesture data, and Unreal Engine is used for gesture recognition. Using the UE4Duino, gesture semantics are transmitted to an Arduino control system, enabling direct control over the robot's movements. For accurate and real-time gesture recognition, we propose a threshold-based method for static gestures and a backpropagation (BP) neural network model for dynamic gestures. In terms of design, the robot utilizes cost-effective thermoplastic polyurethane (TPU) film as the primary pneumatic actuator material. Through a positive and negative pressure switching circuit, the robot's actuators achieve controllable extension and contraction, allowing for basic movements such as linear motion and directional changes. Experimental results demonstrate that the robot can successfully perform diverse motions under gesture control, highlighting the potential of gesture-based interaction in soft robotics.

Neural Network for Enhancing Robot-Assisted Rehabilitation: A Systematic Review

The integration of neural networks into robotic exoskeletons for physical rehabilitation has become popular due to their ability to interpret complex physiological signals. Surface electromyography (sEMG), electromyography (EMG), electroencephalography (EEG), and other physiological signals enable communication between the human body and robotic systems. Utilizing physiological signals for communicating with robots plays a crucial role in robot-assisted neurorehabilitation. This systematic review synthesizes 44 peer-reviewed studies, exploring how neural networks can improve exoskeleton robot-assisted rehabilitation for individuals with impaired upper limbs. By categorizing the studies based on robot-assisted joints, sensor systems, and control methodologies, we offer a comprehensive overview of neural network applications in this field. Our findings demonstrate that neural networks, such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Radial Basis Function Neural Networks (RBFNNs), and other forms of neural networks significantly contribute to patient-specific rehabilitation by enabling adaptive learning and personalized therapy. CNNs improve motion intention estimation and control accuracy, while LSTM networks capture temporal muscle activity patterns for real-time rehabilitation. RBFNNs improve human–robot interaction by adapting to individual movement patterns, leading to more personalized and efficient therapy. This review highlights the potential of neural networks to revolutionize upper limb rehabilitation, improving motor recovery and patient outcomes in both clinical and home-based settings. It also recommends the future direction of customizing existing neural networks for robot-assisted rehabilitation applications.

Building for speech: designing the next-generation of social robots for audio interaction.

There have been significant advances in robotics, conversational AI, and spoken dialogue systems (SDSs) over the past few years, but we still do not find social robots in public spaces such as train stations, shopping malls, or hospital waiting rooms. In this paper, we argue that early-stage collaboration between robot designers and SDS researchers is crucial for creating social robots that can legitimately be used in real-world environments. We draw from our experiences running experiments with social robots, and the surrounding literature, to highlight recurring issues. Robots need better speakers, a greater number of high-quality microphones, quieter motors, and quieter fans to enable human-robot spoken interaction in the wild. If a robot was designed to meet these requirements, researchers could create SDSs that are more accessible, and able to handle multi-party conversations in populated environments. Robust robot joints are also needed to limit potential harm to older adults and other more vulnerable groups. We suggest practical steps towards future real-world deployments of conversational AI systems for human-robot interaction.

Application of entertainment interactive robots based on deep learning in referee assistance mode in sports competitions

Interactive robots are intelligent auxiliary tools that can monitor sports events in real-time and provide entertainment information services. This article studies the application of entertainment interactive robots based on deep learning in the referee assistance mode of sports competitions. The system first uses a camera to capture real-time images of volleyball matches, then preprocesses the images using image processing algorithms, and uses deep learning algorithms to recognize and track the balls and players in the images. By training the model, the system can accurately determine key information such as player actions. Deep learning technology is used to train interactive entertainment robots to identify and analyze key decision events in games. Through image recognition, action analysis and rule matching, the robot monitors the game process in real time and determines the referee's possible errors in the decision. The system generates the penalty results based on the penalty rules and competition rules, and displays them to the referee and audience through display screens or sound prompts. After experimental verification, the volleyball match referee judgment assistance system based on image processing and deep learning has performed excellently in terms of accuracy and speed. Compared to manual referees, the system can identify and track the ball and players more quickly, reduce the possibility of misjudgments, and improve the fairness of the game.

A Large-Area Robotic Skin for Intelligent Tactile Interaction of Collaborative Robots

A Large-Area Robotic Skin for Intelligent Tactile Interaction of Collaborative Robots

Entertainment robots for automatic detection and mitigation of cognitive impairment in elderly populations

This study showed that using collaborative entertainment robots for human-robot interaction can be a promising way to help manage the health of ageing populations by automatically detecting and mitigating cognitive impairment. The system enhanced spoken interaction with users by using cutting-edge technologies such as state-of-the-art speech recognition, natural language processing, and machine learning. The system was tested on senior participants and gathered, analyzed, and displayed individual interaction models to provide automated user engagement, daily interaction monitoring, and automatic early detection of deteriorating mental health. The findings were presented using bar charts and confusion matrices, incorporating important metrics such as mental workload and speech/non-speech interaction graphic. These visualizations aided individuals in managing their behavior to achieve an optimal cognitive workload, a challenging measure to determine due to cognitive decline. In order to make significant progress in the subject, future advancements need to focus on addressing the unpredictability in human speech sequences, using non-speech modalities such as gestures or facial expressions as supplementary inputs to complement speech and behavior, and effectively managing concerns related to human rights and data protection. In addition to technological constraints, future research should prioritize the examination of the enduring impacts of cognitive therapies facilitated by entertainment robots

Days-old zebrafish rapidly learn to recognize threatening agents through noradrenergic and forebrain circuits.

Animals need to rapidly learn to recognize and avoid predators. This ability may be especially important for young animals due to their increased vulnerability. It is unknown whether, and how, nascent vertebrates are capable of such rapid learning. Here, we used a robotic predator-prey interaction assay to show that 1week after fertilization-a developmental stage where they have approximately 1% the number of neurons of adults-zebrafish larvae rapidly and robustly learn to recognize a stationary object as a threat after the object pursues the fish for ∼1min. Larvae continue to avoid the threatening object after it stops moving and can learn to distinguish threatening from non-threatening objects of a different color. Whole-brain functional imaging revealed the multi-timescale activity of noradrenergic neurons and forebrain circuits that encoded the threat. Chemogenetic ablation of those populations prevented the learning. Thus, a noradrenergic and forebrain multiregional network underlies the ability of young vertebrates to rapidly learn to recognize potential predators within their first week of life.

Manufacturing of nanomaterial composites and flexible electronic devices with record high performance for human-technology design, human–computer interaction, biomedical sensing and soft robotics

Flexible electronic devices and technologies are developed using large-scale strategies and nanomaterial composites for human-technology design, human–computer interaction, biomedical and bioelectronics, soft robotics, sensing technologies, etc.

Simulation application of virtual robots and artificial intelligence based on deep learning in enterprise financial systems

In the immersive virtual reality environment, the development of business intelligence models has ushered in new adjustments. Interactive entertainment robots and artificial intelligence can effectively improve the user experience. At the same time, with the advancement of digitalization and networking in enterprise financial systems, the threat of network intrusion poses a serious threat to enterprise financial security. This article analyzes the simulation application of virtual robots and artificial intelligence based on deep learning in enterprise financial systems, and learns how to identify and distinguish between normal network traffic and malicious intrusion behavior. After completing the training of the model, it is deployed to the enterprise financial system to monitor network traffic in real time and perform intrusion detection. The model can analyze and judge the network traffic entering the system in real time, and detect possible network intrusion behaviors based on the learned patterns and features. This paper adopts deep learning algorithm and virtual robot technology to develop a virtual robot system based on deep learning, which can automatically execute financial simulation tasks and generate accurate and reliable financial statements and data analysis results by learning and simulating real enterprise financial data and business processes. By automating financial simulation tasks, virtual robots greatly improve the simulation efficiency and accuracy, and provide more valuable data support for enterprise decision-making. Through experimental testing, it has been found that the network intrusion detection method based on deep neural networks has high accuracy and reliability in enterprise financial systems.

Hip–Knee–Ankle Rehabilitation Exoskeleton With Compliant Actuators: From Human–Robot Interaction Control to Clinical Evaluation

Hip–Knee–Ankle Rehabilitation Exoskeleton With Compliant Actuators: From Human–Robot Interaction Control to Clinical Evaluation

Translation, Adaptation, and Validation in Portuguese of an Acceptance Scale for Human–Robot Interaction in an Industrial Context

Translation, Adaptation, and Validation in Portuguese of an Acceptance Scale for Human–Robot Interaction in an Industrial Context

Design and Validation of an Improved Rotational Variable Stiffness Mechanism

In various aspects of robotics, including human–robot interaction, the ability to dynamically adjust the apparent stiffness of an interaction (e.g., between the robot and its environment or between the robot and its payload) has become an important capability. Various means have been developed in recent years to achieve this, notable among them the so-called variable lever devices. In this paper, we present a new variable lever mechanism based on a gear–rack pair. This unique design combines the functionality of the lever itself with that of the stiffness-adjustment transmission. We show through simulations and hardware experiments the relatively large resulting range of achievable stiffness adjustment and efficient operation.

Robotic companionship for solo diners: the role of robotic service type, need to belong and restaurant type

PurposeWhat kind of robotic service do customers prefer when they dine out alone? This study aims to investigate how robotic service type affects solo diners’ attitude toward robotic service and restaurant revisit intention, through the mediation of rapport. It also examines the moderating effects of the need to belong and restaurant type.Design/methodology/approachThree experiments were conducted. Study 1 used a one-factor between-subjects design to test the effect of robotic service type on rapport and solo diners’ responses. Study 2 conducted a 2 (robotic service type: service-delivery vs entertainment) × need to belong quasi-experimental design to examine the moderation of need to belong. Study 3 used a 2 (robotic service type: service-delivery vs entertainment) × 2 (restaurant type: traditional restaurant vs solo-friendly restaurant) factorial between-subjects design to test the moderation of restaurant type. A qualitative study (Study 4) complements the experimental results based on semistructured interviews.FindingsEntertainment (vs service-delivery) robotic service has a stronger effect on solo diners’ responses, with rapport serving as a mediator. Additionally, solo diners with a heightened need to belong demonstrate an intensified rapport effect when receiving entertainment-oriented robotic service. Furthermore, restaurant type plays a moderating role between robotic service type and consumer responses. For traditional restaurants, solo diners who receive entertainment (vs service-delivery) robotic service tend to form stronger rapport and favorable responses. The results of the qualitative study elucidate and support the hypothesized relationships of the experimental studies.Practical implicationsRestaurant operators could consider offering entertainment-based smart devices that allow solo diners to indulge themselves during the dining encounter. Restaurants could also design environmental cues that can signify a sense of comfort, such as redesigning tables with individual seats for solo diners to enhance their perceptions of shared characteristics among other solo diners in the same space.Originality/valueThis research advances the literature on solo dining and robotic service, by investigating how human–robot interaction can fulfill solo diners’ relatedness goals, as self-determination theory suggests. This inquiry also represents an early attempt in the hospitality literature to empirically examine the influence of robotic service type on consumer responses through the mediation of rapport.