Robot Interaction Research Articles

Enhancing Robot Behavior with EEG, Reinforcement Learning and Beyond: A Review of Techniques in Collaborative Robotics

Collaborative robotics is a major topic in current robotics research, posing new challenges, especially in human–robot interaction. The main aspect in this area of research focuses on understanding the behavior of robots when engaging with humans, where reinforcement learning is a key discipline that allows us to explore sophisticated emerging reactions. This review aims to delve into the relevance of different sensors and techniques, with special attention to EEG (electroencephalography data on brain activity) and its influence on the behavior of robots interacting with humans. In addition, mechanisms available to mitigate potential risks during the experimentation process such as virtual reality are also be addressed. In the final part of the paper, future lines of research combining the areas of collaborative robotics, reinforcement learning, virtual reality, and human factors are explored, as this last aspect is vital to ensuring safe and effective human–robot interactions.

Development of Standalone Extended-Reality-Supported Interactive Industrial Robot Programming System

Extended reality (XR) is one of the most important technologies in developing a new generation of human–machine interfaces (HMIs). In this study, the design and implementation of a standalone interactive XR-supported industrial robot programming system using the Unity game engine is presented. The presented research aims to achieve a cross-platform solution that enables novel tools for robot programming, trajectory validation, and robot programming debugging within an extended reality environment. From a robotics perspective, key design tasks include modeling in the Unity environment based on robot CAD models and control design, which include inverse kinematics solution, trajectory planner development, and motion controller set-up. Furthermore, the integration of real-time vision, touchscreen interaction, and AR/VR headset interaction are involved within the overall system development. A comprehensive approach to integrating Unity with established industrial robot modeling conventions and control strategies is presented. The proposed modeling, control, and programming concepts, procedures, and algorithms are verified using a 6DoF robot with revolute joints. The benefits and challenges of using a standalone XR-supported interactive industrial robot programming system compared to integrated Unity–robotics development frameworks are discussed.

User segmentation in human–robot interactions: insights from sports betting patrons using diffusion of innovation theory

ABSTRACT Previous research has assumed uniformity in user response to service robots regardless of user profile. This study sought to understand the nature of human–robot interactions in a competitive setting by applying the diffusion of innovation theory. Through a survey of 447 sports betting patrons, we used unsupervised learning to cluster the patrons based on human/robot interaction, relational and psychological variables, usage intention, and socioeconomic information. Four clusters were identified: Tech laggards, Optimists, Skeptics, and Enthusiasts. Trust was a key determining factor of cluster membership, especially among frequent betters. Further, the study examined each cluster through the lens of psychological theories and ethical perspectives, identified associated risks, and offered mitigation strategies. Practical implications are discussed.

Intelligent Agents at School—Child–Robot Interactions as an Educational Path

The pervasiveness of technologies leads us to talk about a code society. From an educational point of view, coding, computational thinking, and educational robotics are an open possibility. Nevertheless, new elements such as artificial intelligence are rapidly changing educational technology perspectives. In this work, we will analyze school policies and theoretical bases in order to understand if, and under what kind of, condition coding, computational thinking, and educational robotics still represent the qualifying elements of a framework for digital literacy and digital citizenship.

Inferring Human Control Intent Using Inverse Linear Quadratic Regulator With Output Penalty Versus Gain Penalty: Better Fit but Similar Intent

Abstract To identify the underlying mechanisms of human motor control, parametric models are utilized. One approach of employing these models is the inferring the control intent (estimating motor control strategy). A well-accepted assumption is that human motor control is optimal; thus, the intent is inferred by solving an inverse optimal control (IOC) problem. Linear quadratic regulator (LQR) is a well-established optimal controller, and its inverse LQR (ILQR) problem has been used in the literature to infer the control intent of one subject. This implementation used a cost function with gain penalty, minimizing the error between LQR gain and a preliminary estimated gain. We hypothesize that relying on an estimated gain may limit ILQR optimization capability. In this study, we derive an ILQR optimization with output penalty, minimizing the error between the model output and the measured output. We conducted the test on 30 healthy subjects who sat on a robotic seat capable of rotation. The task utilized a physical human–robot interaction with a perturbation torque as input and lower and upper body angles as output. Our method significantly improved the goodness of fit compared to the gain-penalty ILQR. Moreover, the dominant inferred intent was not statistically different between the two methods. To our knowledge, this work is the first that infers motor control intent for a sample of healthy subjects. This is a step closer to investigating control intent differences between healthy subjects and subjects with altered motor control, e.g., low back pain.

Enhancing Sensitivity across Scales with Highly Sensitive Hall Effect‐Based Auxetic Tactile Sensors

The research addresses the limitations inherent in conventional Hall effect‐based tactile sensors, particularly their restricted sensitivity by introducing an innovative metastructure. Through meticulous finite element analysis optimization, the Hall effect‐based auxetic tactile sensor (HEATS), featuring a rotating square plate configuration as the most effective auxetic pattern to enhance sensitivity, is developed. Experimental validation demonstrates significant sensitivity enhancements across a wide sensing range. HEATS exhibits a remarkable 20‐fold and 10‐fold improvement at tensile rates of 0.9% and 30%, respectively, compared to non‐auxetic sensors. Furthermore, comprehensive testing demonstrates HEATS’ exceptional precision in detecting various tactile stimuli, including muscle movements and joint angles. With its unparalleled accuracy and adaptability, HEATS offers vast potential applications in human–machine and human–robot interaction, where subtle tactile communication is a prerequisite.

Social Assistive Robotics: An Ethical and Political Inquiry Through the Lens of Freedom

The development of social assistive robots for supporting healthcare provision faces a lack of an ethical approach that adequately addresses the normatively relevant challenges regarding its deployment. Current ethical reflection is primarily informed by an individual-centered perspective focused on robots’ implications for their end-users and thereby limited to the dyadic human–robot interaction sphere. Considering that this is tightly correlated to the restricted understanding of core ethical concepts upon which reflection stands, this paper delves into the concept of freedom from a philosophical perspective to unfold its full normative breadth for a critical assessment of technological development. By bringing to the fore the political-structural dimension of freedom and, in turn, elaborating the political dimension of technology, the undertaken philosophical approach discloses freedom as a transversal ethical concept for a normative reflection on technology. Thereby, it broadens the scope of ethical attention beyond the sphere of human–robot interaction and turns attention to the so far overlooked structural dimension of human–robot relations. Drawing on conceptions of freedom as non-domination, among others, the paper approaches social assistive robotics and reexamines the terrain of relevant issues for its development. Since freedom is one major issue upon which current concerns revolve, the undertaken analysis substantially enriches the ongoing ethical discussion on social assistive robotics’ implications for human freedom. In this way, this work contributes to going beyond the current individual-centered ethical perspective by laying conceptual grounds for a comprehensive ethical approach to social assistive robotics’ development.

Enhancing Autonomous Driving Robot Systems with Edge Computing and LDM Platforms

The efficient operation and interaction of autonomous robots play crucial roles in various fields, e.g., security, environmental monitoring, and disaster response. For these purposes, processing large volumes of sensor data and sharing data between robots is essential; however, processing such large data in an on-device environment for robots results in substantial computational resource demands, causing high battery consumption and heat issues. Thus, this study addresses challenges related to processing large volumes of sensor data and the lack of dynamic object information sharing among autonomous robots and other mobility systems. To this end, we propose an Edge-Driving Robotics Platform (EDRP) and a Local Dynamic Map Platform (LDMP) based on 5G mobile edge computing and Kubernetes. The proposed EDRP implements the functions of autonomous robots based on a microservice architecture and offloads these functions to an edge cloud computing environment. The LDMP collects and shares information about dynamic objects based on the ETSI TR 103 324 standard, ensuring cooperation among robots in a cluster and compatibility with various Cooperative-Intelligent Transport System (C-ITS) components. The feasibility of operating a large-scale autonomous robot offloading system was verified in experimental scenarios involving robot autonomy, dynamic object collection, and distribution by integrating real-world robots with an edge computing–based offloading platform. Experimental results confirmed the potential of dynamic object collection and dynamic object information sharing with C-ITS environment components based on LDMP.

Optimal design and fabrication of frame structure for dual-arm service robots: An effective approach for human–robot interaction

Rapid advancement in robotics technology has paved the way for developing mobile service robots capable of human interaction and assistance. In this paper, we propose a comprehensive approach to design, fabricate, and optimize the overall structure of a dual-arm service robot. The conceptual design phase focuses on both critical components, the mobile platform and the manipulation system, essential for seamless navigation and effective task execution. In the proposed system, the distribution of the robot payload in terms of region, maximum stress, and displacement is examined, comprehensively analyzed, and compared with the relevant works. In addition, to enhance the system’s efficiency while minimizing its weight, we introduce a lightweight design approach in which Finite Element Analysis is utilized to optimize the frame structure. Subsequently, we fabricate a physical prototype based on the derived model. Finally, we provide a kinematic model for our dual-arm service robot and demonstrate its efficacy in both control and human–robot interaction (HRI) tasks. Experimental results indicate that the proposed dual arm design can achieve a significant weight reduction of 25% from the original design while still performing actions smoothly for HRI tasks.

A Review on the Form and Complexity of Human–Robot Interaction in the Evolution of Autonomous Surgery

As robotics and intelligence increasingly integrate into surgery, the pivotal role of human–robot interaction (HRI) in surgical procedures and outcomes becomes evident. However, debate rages over whether increasing robot autonomy will result in less human involvement. Some scholars assert that autonomy will reduce human participation, whereas others contend it will result in more complex interactions. To reveal the role of HRI in the evolution of autonomous surgery, this review systematically explores the HRI of robotic surgery with various levels of autonomy. The HRI is examined from both robotic science and clinical practice perspectives, incorporating relevant case studies. Two key components, intention detection and situation awareness, are especially concerned with a brief description of the interfaces and control strategies they rely on. Additional insights are drawn from analogous technologies in aviation, industrial robotics, and autonomous vehicles. The analysis suggests that HRI complexity tends to increase as the robot transitions from no autonomy to conditional autonomy and is predicted to subsequently decrease with a substantial shift in the interaction form when moving toward full autonomy. It is concluded by highlighting challenges from technical and clinical perspectives and delineating research trends in this rapidly evolving field.

The Role of Human-Robot Interaction in Enhancing Efficiency and Safety in Industrial Robotics

This study examines the role of human-robot interaction (HRI) in enhancing efficiency and safety in industrial robotics. Emphasize the importance of efficiency and safety in industrial robotics, underscoring the critical role in improving productivity, reducing costs, and ensuring the well-being of workers. The results were analyzed inline with the four basic robot tasks' completion time. The error rates, collision risks, ergonomic strain, and overall productivity were determined. From the evaluation of the time required to complete each task in the context of human-robot interaction and optimize the workflow to enhance productivity and safety, it was observed that the average task completion time for task 1 was 10.63 minutes; while task 4 was 16.39 minutes. This indicates that some critical tasks require additional resources or adjustments to streamline the process and improve overall performance. In the determination of the safety rating, for each task by subtracting the error rate from 100% performance, it was observed that task 2 has a safety rating of 98% while task 3 has a safety rating of 92%, this indicates that the safety rating is different with respect to a different task. The productivity metrics for each task were determined by multiplying the task completion rate by the task efficiency rate. Hence, the Average Productivity Rating was 0.7449 which indicate the overall productivity in the role of human-robot interaction in enhancing efficiency and safety in industrial robotics. The integration of human-robot interaction in industrial robotics offers a synergistic approach to improving efficiency and safety in manufacturing environments.

Application of entertainment interactive robot based on speech recognition in English artificial intelligence teaching evaluation and automatic feedback

With the development of intelligent voice and interactive robot technology, new technologies have built a virtual E-learning learning environment that can provide students with an immersive learning experience, making this new learning mode more entertaining. This article investigates the application of entertainment interactive robots based on speech recognition in English artificial intelligence teaching evaluation and automatic feedback. The system has constructed an oral evaluation model based on deep reinforcement learning, which learns the optimal behavioral strategies through interaction with the environment. The model will train through oral conversations with learners to learn how to accurately evaluate oral proficiency and provide relevant feedback. After the construction of the system is completed, the accuracy and efficiency of the system are improved by adjusting the parameters of the model, increasing the diversity of training data, and improving the user interface and interaction mode based on user feedback, making it more friendly and easy to use. The experimental results show that the English oral evaluation and automatic feedback system designed in this paper based on deep reinforcement learning and speech recognition algorithms has high accuracy and efficiency. The system can accurately evaluate learners’ oral proficiency and provide personalized learning suggestions based on individual differences.

Robot remote control using virtual reality headset: studying sense of agency with subjective distance estimates

Mobile robots have many applications in the modern world. The autonomy of robots is increasing, but critical cases like search and rescue missions must involve the possibility of human intervention for ethical reasons and safety. To achieve effective human–robot interaction, the operator needs to have a sense of agency (SoA) over the activities of the robot. One possible way to increase one's SoA in remote control could be the use of VR technology. The remote control situation has some important features, so indicators of SoA need to be reproduced there independently. In our study, participants controlled a mobile robot using either a monitor or a VR-headset as an output device. In both cases, active control was contrasted with passive observation of the robot's movement. In each trial, participants estimated the distance traveled by the robot—a putative implicit indicator of SoA. A significant difference between subjective distance estimates was found in the active and passive conditions with the monitor, but not in the active and passive conditions with VR. The effect obtained in the monitor conditions suggests that distance estimates can be used as an implicit indicator of SoA in robot remote control. We believe that the lack of difference between the active and passive conditions in VR was caused by motion sickness due to a mismatch of visual and vestibular sensory cues, leading to a weakened SoA.

Robotic Assistance and Haptic Feedback in Arthroscopic Procedures: Design and Preliminary Evaluation of a Two-Arm System

Robot-assisted arthroscopic surgery has been receiving growing attention in the field of orthopedic surgery. Most of the existing robot-assisted surgical systems in orthopedics place more focus on open surgery than minimally invasive surgery (MIS). In traditional arthroscopic surgery, the surgeon needs to hold an arthroscope with one hand while performing the surgical operations with the other hand, which can restrict the dexterity of the surgical performance and increase the cognitive load. On the other hand, the surgeon heavily relies on the arthroscope view when conducting the surgery, whereas the arthroscope view is a largely localized view and lacks depth information. To assist the surgeon in both scenarios, in this work, we develop a two-arm robotic system. The left-arm robot is used as a robot-assisted arthroscope holder, and it can hold the arthroscope still at a designated pose and reject all other potential disturbances, while also allowing the operator to move it via a pedal switch whenever needed. The left-arm robot is implemented with an impedance controller and a gravity iterative learning (Git) scheme, where the former can provide compliant robot behavior, thus ensuring a safe human–robot interaction, while the latter can accurately learn for gravity compensation. The right-arm robot is used as a robot-assisted surgical tool, providing virtual fixture (VF) assistance and haptic feedback during the surgery. The right-arm robot is implemented with a point-based VF algorithm, which can generate VF directly from point clouds in any shape, render force feedback, and deliver it to the operator. Furthermore, the VF, the bone, and the surgical tool position are visualized in a 3D digital environment as additional visual feedback for the operator. A series of experiments are conducted to evaluate the effectiveness of the prototype. The results demonstrate that both arms can provide satisfactory assistance as designed.

Design and Analysis of a Symmetric Joint Module for a Modular Wire-Actuated Robotic Arm with Symmetric Variable-Stiffness Units

Collaborative robots are used in scenarios requiring interaction with humans. In order to improve the safety and adaptability of collaborative robots during human–robot interaction, this paper proposes a modular wire-actuated robotic arm with symmetric variable-stiffness units. The variable-stiffness unit is employed to extend the stiffness-adjustment range of the robotic arm. The variable-stiffness unit is designed based on flexure, featuring a compact and simple structure. The stiffness–force relationship of the variable-stiffness unit can be fitted by a quadratic function with an R-squared value of 0.99981, indicating weak nonlinearity. Based on the kinematics and stiffness analysis of the symmetric joint module of the robotic arm, the orientation of the joint module can be adjusted by regulating the length of the wires and the stiffness of the joint module can be adjusted by regulating the tension of the wires. Because of the actuation redundancy, the orientation and stiffness of the joint module can be adjusted synchronously. Furthermore, a direct method is proposed for the stiffness-oriented wire-tension-distribution problem of the 1-DOF joint module. A simulation is carried out to verify the proposed method. The simulation result shows that the deviation between the calculated stiffness and the desired stiffness was less than 0.005%.

Navigating the spectrum of human-robot collaboration: Addressing robophobia-robophilia in the hospitality industry

Analyzing human-robot collaboration, this study draws on insights from cognitive science and psychology, with a particular focus on developing strategies for instructing robots and addressing the dynamics of robophobia and robophilia. Robophilia signifies the evolving, positive human-robot relationship, emphasizing the attribution of personhood to robots, while robophobia represents fear of robots. Although hospitality studies have explored anthropomorphism in robots, there is a gap in understanding robophobia impact in this context. This research investigates the transition from robophilia to robophobia and its implications for human-robot interactions in hospitality. Past studies primarily emphasized positive aspects, like anthropomorphism and robot personhood neglecting potential negatives such as robophobia. Guided by analogical transfer theory, fear acquisition theory, and uncanny valley theory using three experimental studies, this research explore factors driving this transition, its impact on embarrassment, customer outrage, and employee sabotage intentions during joint robot interactions. This research enriches our understanding of complex human-robot dynamics, shedding light on robophobia's mediating role within the context of service disruptions.

Beyond simulation: Unlocking the frontiers of humanoid robot capability and intelligence with Pepper's open-source digital twin

This research paper presents a high-fidelity, open-source digital-twin of the Pepper robot developed within the framework of the Robot Operating System 2 (ROS 2) for better simulation realism in complex tasks of machine learning. We developed a dedicated, custom ROS 2 package with modern simulation tools, such as Gazebo Sim, MoveIt 2, Rviz2, that brings complete, realistic environments in line with the exact behaviors and interactions of robots in reality. Better accuracy of the physical movement of Pepper robot's simulation was shown on the digital twin, validated by the Choregraphe software and real robot performance, to be a strong platform of collaboration and further research by the community. This development greatly pushes the envelope of human-like humanoid robotics further by offering a scaled, flexible, and plausible training environment conducive to integrating complex algorithms of robot learning and interaction capabilities.

Application of entertainment and fitness robots based on game interaction in sports training data analysis

This study aims to explore the application of sports data analysis of recreational fitness robots in sports training. By introducing interactive elements of games, recreational fitness robots can provide more interesting and challenging fitness experience, thus attracting more people to participate in sports training. Research and design and develop a game interactive entertainment fitness robot, with action recognition and user interaction functions, can perceive the user’s actions and make corresponding responses. When the user interacts with the robot, the sensor continuously collects and records data, establishes a data acquisition and storage system, analyzes and processes the collected interactive data, identifies the user’s movement pattern, assesses the user’s physical fitness level, and identifies potential improvement points. According to the user’s physical condition and training objectives, the appropriate exercise plan and game interaction mode are designed to provide better training results and entertainment experience. It is found that the game-based interactive entertainment fitness robot can effectively improve the user’s exercise motivation and participation in sports training. Through the analysis of sports data, we can develop personalized training plans according to the specific needs and goals of users, so as to achieve better training results.

A simulation‐assisted point cloud segmentation neural network for human–robot interaction applications

AbstractWith the advancement of industrial automation, the frequency of human–robot interaction (HRI) has significantly increased, necessitating a paramount focus on ensuring human safety throughout this process. This paper proposes a simulation‐assisted neural network for point cloud segmentation in HRI, specifically distinguishing humans from various surrounding objects. During HRI, readily accessible prior information, such as the positions of background objects and the robot's posture, can generate a simulated point cloud and assist in point cloud segmentation. The simulation‐assisted neural network utilizes simulated and actual point clouds as dual inputs. A simulation‐assisted edge convolution module in the network facilitates the combination of features from the actual and simulated point clouds, updating the features of the actual point cloud to incorporate simulation information. Experiments of point cloud segmentation in industrial environments verify the efficacy of the proposed method.

MoVEInt: Mixture of Variational Experts for Learning Human–Robot Interactions From Demonstrations

MoVEInt: Mixture of Variational Experts for Learning Human–Robot Interactions From Demonstrations