Robot Interaction Research Articles

Guest editorial: The current landscape and research priorities of human–robot interactions in hospitality and tourism

Guest editorial: The current landscape and research priorities of human–robot interactions in hospitality and tourism

BEATRIX: An open source humanoid head platform for robotics teaching and research

This paper introduces BEATRIX, a novel robotic head designed to bridge the gap between theoretical knowledge and practical experience in the field of robotics at universities. The BEATRIX robot comprises a head actuated by a neck-like mechanism with three stepper motors, two cameras and two microphones for acquisition of visual and audio information from the environment. The robot can be connected to any external computer for the design and implementation of algorithms for applications in human–robot interaction. The proposed robotic platform has been used successfully with undergraduate and master students implementing tasks such as face detection and tracking, sound detection and tracking, robot control and graphical user interfaces. This paper includes lists of all the robot components, assembly instructions, and links to all CAD and software files, facilitating replication and further exploration. The robot design and integration of visual and audio sensors enables the development of engaging educational tutorials and robot experiments, enhancing the teaching and learning experience in robotics.

Stiffness-fault-tolerant control strategy for elastic actuators with interaction impedance adaptation

Elastic actuators have the potential to enable safe interaction and energy efficient mobility, making them suitable for physical human–robot interaction. However, their increased complexity makes technical faults and their prevention a relevant research topic, particularly considering faults in elastic and kinematic elements. In this article we investigate a stiffness-fault-tolerant control strategy for elastic actuators, based on impedance control, which compensates for internal faults and adapts to a desired interaction impedance behavior. We analyze the control strategy regarding its stability, and adapt it to the dynamic characteristics of two systems: a mechanically adjustable compliance actuator (MACCEPA) and a series–parallel elastic actuator (+SPEA), highlighting the strategy’s general applicability to multiple actuator designs, considering nonlinear and redundant characteristics. Experimental validation with these systems shows that the control strategy is capable of accurately tracking reference output trajectories and adapting interaction characteristics, under fault and disturbance conditions, showcasing the versatile applicability of the strategy while achieving fault-tolerance.

A Knowledge Transfer-Based Personalized Human–Robot Interaction Control Method for Lower Limb Exoskeletons

A Knowledge Transfer-Based Personalized Human–Robot Interaction Control Method for Lower Limb Exoskeletons

Multimodal Human–Robot Interaction Using Gestures and Speech: A Case Study for Printed Circuit Board Manufacturing

In recent years, technologies for human–robot interaction (HRI) have undergone substantial advancements, facilitating more intuitive, secure, and efficient collaborations between humans and machines. This paper presents a decentralized HRI platform, specifically designed for printed circuit board manufacturing. The proposal incorporates many input devices, including gesture recognition via Leap Motion and Tap Strap, and speech recognition. The gesture recognition system achieved an average accuracy of 95.42% and 97.58% for each device, respectively. The speech control system, called Cellya, exhibited a markedly reduced Word Error Rate of 22.22% and a Character Error Rate of 11.90%. Furthermore, a scalable user management framework, the decentralized multimodal control server, employs biometric security to facilitate the efficient handling of multiple users, regulating permissions and control privileges. The platform’s flexibility and real-time responsiveness are achieved through advanced sensor integration and signal processing techniques, which facilitate intelligent decision-making and enable accurate manipulation of manufacturing cells. The results demonstrate the system’s potential to improve operational efficiency and adaptability in smart manufacturing environments.

Activities of Daily Living Object Dataset: Advancing Assistive Robotic Manipulation with a Tailored Dataset

The increasing number of individuals with disabilities—over 61 million adults in the United States alone—underscores the urgent need for technologies that enhance autonomy and independence. Among these individuals, millions rely on wheelchairs and often require assistance from another person with activities of daily living (ADLs), such as eating, grooming, and dressing. Wheelchair-mounted assistive robotic arms offer a promising solution to enhance independence, but their complex control interfaces can be challenging for users. Automating control through deep learning-based object detection models presents a viable pathway to simplify operation, yet progress is impeded by the absence of specialized datasets tailored for ADL objects suitable for robotic manipulation in home environments. To bridge this gap, we present a novel ADL object dataset explicitly designed for training deep learning models in assistive robotic applications. We curated over 112,000 high-quality images from four major open-source datasets—COCO, Open Images, LVIS, and Roboflow Universe—focusing on objects pertinent to daily living tasks. Annotations were standardized to the YOLO Darknet format, and data quality was enhanced through a rigorous filtering process involving a pre-trained YOLOv5x model and manual validation. Our dataset provides a valuable resource that facilitates the development of more effective and user-friendly semi-autonomous control systems for assistive robots. By offering a focused collection of ADL-related objects, we aim to advance assistive technologies that empower individuals with mobility impairments, addressing a pressing societal need and laying the foundation for future innovations in human–robot interaction within home settings.

Time-Varying Preference Bandits for Robot Behavior Personalization

Robots are increasingly employed in diverse services, from room cleaning to coffee preparation, necessitating an accurate understanding of user preferences. Traditional preference-based learning allows robots to learn these preferences through iterative queries about desired behaviors. However, these methods typically assume static human preferences. In this paper, we challenge this static assumption by considering the dynamic nature of human preferences and introduce the discounted preference bandit method to manage these changes. This algorithm adapts to evolving human preferences and supports seamless human–robot interaction through effective query selection. Our approach outperforms existing methods in time-varying scenarios across three key performance metrics.

Robots and Dance: A Promising Young Alchemy

Research at the intersection of robots and dance promises to create vehicles for expression that enable new creative pursuits and allow robots to function better, especially in human-facing scenarios. Moving this research beyond fringe spectacle and establishing it as a serious, systematic field—a proper subdiscipline of both robotics and dance—will require answering a key question: How does dance advance the fundamentals of robotics, and vice versa? Focusing on the former, this article offers glimpses of this new field with examples of meaningful contributions to control, robotics, and autonomous systems, such as novel actuator designs, improved sensing systems, salient motion profiles for robots, reproducible experiment designs, and new theories of motion derived from the study of dance. It also poses two grand challenges for the emerging field of choreobotics: developing a robust symbolic system for representing bodily action and establishing rich, repeatable testing environments for human–robot interaction.

Deep Reinforcement Learning-Assisted Teaching Strategy for Industrial Robot Manipulator

This paper introduces an innovative algorithm aimed at enhancing robot learning using dynamic trajectory modeling and time-dependent state analysis. By integrating reinforcement learning (RL) and trajectory planning, the proposed approach enhances the robot’s adaptability in diverse environments and tasks. The framework begins with a comprehensive analysis of the robot’s operational space, focusing on Cartesian coordinates and configuration systems. By modeling trajectories and states within these systems, the robot achieves sequential tracking of arbitrary states, facilitating efficient task execution in various scenarios. Experimental results demonstrate the algorithm’s efficacy in manipulation tasks and path planning in dynamic environments. By integrating dynamic trajectory modeling and time-dependent state analysis, the robot’s adaptability and performance improve significantly, enabling precise task execution in complex environments. This research contributes to advancing robot learning methodologies, particularly in human–robot interaction scenarios, promising applications in manufacturing, healthcare, and logistics.

Riding on uncertainty: Leveraging human agents and service robots during service delivery

This study examines customer response to human and robot service encounters under different service uncertainty levels. Guided by uncertainty management and service encounter needs theories, we argue that service uncertainty will moderate the effect of service encounters (humans vs. robots) on the perceived utility and rapport of customers. The results of two experiments show that under high service uncertainty, robots are perceived by customers to have higher utility than humans. Meanwhile, when the service uncertainty level is low, human agents will be perceived to demonstrate higher rapport than robots. The results further show that service uncertainty can moderate the mediating effect of perceived utility and rapport in the relationship between service encounters and customer satisfaction. Acknowledging service uncertainty, we identify the theoretical implications of human–robot interactions and provide practical suggestions to hospitality and service companies for devising effective strategies to manage the technological transformation of their service delivery.

A safety posture field framework for mobile manipulators based on human–robot interaction trend and platform-arm coupling motion

Mobile manipulators are increasingly deployed in industrial settings, such as material handling and workpiece loading, where they must safely interact with humans while efficiently completing tasks. Existing motion planning methods for mobile manipulators often struggle to ensure both safety and efficiency in dynamic human-robot interaction environments. This paper proposes a Safety Posture Field framework that addresses these limitations by firstly predicting human motion trends using the improved Long Short-Term Memory neural network and applying these predictions to potential field calculations for both the mobile platform and the robotic arm. During different stages of human-robot interaction, the mobile manipulator places varying emphasis on safety and efficiency while in motion. Additionally, when the robotic arm executes operations, a platform-arm coupling motion strategy is introduced when the potential field detects risks of singularity or local optima, preventing the robotic arm from becoming unstable or failing to reach the target pose in time. This strategy enhances the system's flexibility and operational stability. Comparative experiments in simulation and real-world settings confirm the ability of the framework to maintain high safety standards while improving task efficiency, making it suitable for industrial Human-Robot Interaction applications.

How do older adults react to social robots’ offspring-like voices

Social robots are being developed as a technological solution to alleviate older adults' loneliness due to separation from their offspring. This study explores how and why offspring-like voices affect older adults' acceptance of social robots from an auditory perspective, which differ from the visual aspects of human–robot interactions. Three scenario-based studies are conducted among a large number of cognitively intact older adults. Our findings reveal a positive correlation between the offspring-like voices of social robots and older adults’ acceptance of the robots. Further, social identity served as a psychological mechanism mediating the effect of offspring-like voices on the acceptance of older adults, whereas spatial distance acted as a positive moderator of these direct and indirect effects. Notably, older adults were more willing to accept social robots with grandchildren-like voices. These insights offer theoretical contributions to the literature on social identity theory and the similarity attraction paradigm, as well as practical implications for social robot design and development, thereby contributing to the evolving landscape of human–robot interaction acceptance.

Can cuteness soften my anxiety? The impact of conversational styles of service robots on consumer service acceptance

Purpose As artificial intelligence technology empowers service robots, they increasingly communicate with consumers in a human-like manner. This study aims to investigate the effect of service robots’ different conversational styles (competent conversational style vs. cute conversational style) on consumer service acceptance and demonstrate the moderating role of consumers’ technology anxiety. Design/methodology/approach Based on anthropomorphism theory and social presence theory, the authors conducted two scenario-based experiments (restaurant scenario and hotel scenario) to investigate this issue. Findings The results indicate that service robots’ conversational styles impact consumers’ willingness to accept the use of service robots through perceived social presence and positive emotion. Moreover, consumers perceived social presence and positive emotion play a serial mechanism. In addition, the effect of competent conversational style on consumers perceived social presence is less effective than that of cute conversational style. Finally, the authors demonstrate the moderating role of consumer technology anxiety in the relationship between conversational styles and perceived social presence. Practical implications To provide consumers with a positive human–robot interaction experience at the service front line, managers need to make better use of the conversational styles of service robots by comprehensively considering the characteristics of consumer technology anxiety. Originality/value This research extends the literature on service robots by integrating consumer characteristics and robots’ conversational styles. These findings highlight the effectiveness of cute conversational style in alleviating consumer technology anxiety.

RIGTA: Interactive Robot as an Integration Platform for Engineering Education

RIGTA: Interactive Robot as an Integration Platform for Engineering Education

Industrial exoskeletons for secure human–robot interaction: a review

Industrial exoskeletons for secure human–robot interaction: a review

Design and Experimental Evaluation of Multiple 3D-Printed Reduction Gearboxes for Wearable Exoskeletons

The recent advancements in wearable exoskeletons have highlighted their effectiveness in assisting humans for both rehabilitation and augmentation purposes. These devices interact with the user; therefore, their actuators and power transmission mechanisms are crucial for enhancing physical human–robot interaction (pHRI). The advanced progression of 3D printing technology as a valuable method for creating lightweight and efficient gearboxes enables the exploration of multiple reducer designs. However, to the authors’ knowledge, only sporadic implementations with relatively low reduction ratios have been reported, and the respective experimental validations usually vary, preventing a comprehensive evaluation of different design and implementation choices. In this paper, we design, develop, and examine experimentally multiple 3D-printed gearboxes conceived for wearable assistive devices. Two relevant transmission ratios (1:30 and 1:80) and multiple designs, which include single- and double-stage compact cam cycloidal drives, compound planetary gearboxes, and cycloidal and planetary architectures, are compared to assess the worth of 3D-printed reducers in human–robot interaction applications. The resulting prototypes were examined by evaluating their weight, cost, backdrivability, friction, regularity of the reduction ratio, gear play, and stiffness. The results show that the developed gearboxes represent valuable alternatives for actuating wearable exoskeletons in multiple applications.

Super‐adaptive electroactive programmable adhesive materials to challenging surfaces: From intelligent soft robotics to XR haptic interfaces

AbstractRecently, the intelligent strategies for adapting to multiple challengeable surfaces of electroactive programmable materials integrated with bio‐inspired architectures offer expanded directions beyond traditional limitations in soft grippers, medical mobile robots, and XR (Extended Reality) interfaces. These electroactive programmable adhesive materials are adaptively designed for a variety of complex surfaces, including soft, wet, non‐flat, or contamination‐susceptible feature such as bio‐surfaces and vulnerable objects. They can be produced via solution‐based methods of replica coating or 3/4‐dimensional printing. The integration of electroactive programmable materials and intelligent adhesive architecture enables super‐adaptive switchable adhesion to a variety of complex surfaces through control of physical deformation and mechanical properties at the adhesive interface, presenting a breakthrough in soft electro‐robotics and extended reality (XR) Haptic interfaces technology. These surface‐adaptive platform can provide multiple functionalities that can efficiently control physical deformations of soft bioinspired architectures or transfer physical energy (heat, vibration, pressure) into the engaged surfaces in a lightweight and human‐friendly form. This review focuses on intelligent strategies, principles, design, and fabrication methods of super‐adaptive electroactive programmable materials intelligently combined with bioinspired switchable adhesives for next‐generation human–robot interaction devices, along with current challenges and prospects.image

LEVIOSA: Natural Language-Based Uncrewed Aerial Vehicle Trajectory Generation

This paper presents LEVIOSA, a novel framework for text- and speech-based uncrewed aerial vehicle (UAV) trajectory generation. By leveraging multimodal large language models (LLMs) to interpret natural language commands, the system converts text and audio inputs into executable flight paths for UAV swarms. The approach aims to simplify the complex task of multi-UAV trajectory generation, which has significant applications in fields such as search and rescue, agriculture, infrastructure inspection, and entertainment. The framework involves two key innovations: a multi-critic consensus mechanism to evaluate trajectory quality and a hierarchical prompt structuring for improved task execution. The innovations ensure fidelity to user goals. The framework integrates several multimodal LLMs for high-level planning, converting natural language inputs into 3D waypoints that guide UAV movements and per-UAV low-level controllers to control each UAV in executing its assigned 3D waypoint path based on the high-level plan. The methodology was tested on various trajectory types with promising accuracy, synchronization, and collision avoidance results. The findings pave the way for more intuitive human–robot interactions and advanced multi-UAV coordination.

Dynamic estimation of an object's center-of-mass direction: a novel control method for robotic interaction in uncertain environments

To operate in uncertain environments, robots must dynamically interact with and recognize objects. This paper proposes a novel control method to dynamically estimate the center-of-mass of an uncertain object. In this method, a robot finger moves an object, and the moving direction of the finger is dynamically adjusted to estimate the direction of the object's center-of-mass (friction center). The primary advantage of this method is its capacity to rapidly estimate the center-of-mass direction utilizing a single contact. Once the direction of the center-of-mass is determined, this information can be used, for example, for the robot hand to grasp the object securely by surrounding its center-of-mass, ensuring stable handling without unexpected movements. Moreover, determining the center-of-mass can aid in automatically producing training data for machine learning applications. Both simulation and experimental results validate the proposed control method's efficacy, demonstrating its ability to quickly converge to the desired state. This control problem poses a significant challenge as the system has fewer actuators than degrees of freedom, indicating that this controlled system is underactuated. Nonetheless, the proposed control method operated successfully.

Optimizing Communication for a Humanoid Subs-Talker Robot Using RSSI Analysis on ROS System

This study aims to optimize communication for the humanoid "Subs-Talker" robot using Receive Signal Strength Indicator (RSSI) analysis within the Robot Operating System (ROS). The background of this research highlights the importance of stable and strong inter-robot communication in the Indonesian Humanoid Robot Soccer Contest (KRSBI-H), where robots need to function in synergy. The method involves measuring signal strength at specific distances, both with and without the use of an extender device, utilizing the InSSIDer application for RSSI data recording and analysis. The results show that using an extender improves communication stability, especially at longer distances (7.2 and 9.0 meters), although the impact is minimal at closer distances. The discussion suggests that an extender can be an effective solution for enhancing inter-robot communication quality in competitions. In conclusion, the extender significantly improves the communication range and signal quality among robots, supporting optimal performance in collaborative robot interactions within the competition arena.