Virtual Robot Research Articles

Autonomous Cooperative Hunting with Rule-Based and Self-Learning Control for Multiagent Systems

This paper considers the problem of autonomous cooperative hunting in an unknown dynamic environment, where a group of mobile agents collaborate to capture a moving target. Due to the decentralized decision-making nature of multi-agent systems and the presence of real-world constraints, it is a challenging task. To solve this problem, an artificial rule based hunting algorithm (AR-HA) is firstly developed based on the principles of attraction and repulsion with heading adjustment, and each agent is controlled by the designed rules. Then, to further enhance the stability of cooperative hunting, a self-learning algorithm based on Twin Delayed Deep Deterministic policy gradient (SL-TD3) is proposed. Each agent is governed by its own SL-TD3 controller and learns independently from its interaction with the environment, taking advantage of the reward function designed based on the control rules of AR-HA. Besides, in order to improve training efficiency, imitation learning is employed to initialize the actor network. Experiments on both virtual and real robots demonstrate the effectiveness of the proposed algorithms for autonomous cooperative hunting.

I Care That You Don’t Share: Confidentiality in Student-Robot Interactions

Enabled by technological advances, robot teachers have entered educational service frontlines. Scholars and policymakers suggest that during Human-Robot Interaction (HRI), human teachers should remain “in-the-loop” (i.e., oversee interactions between students and robots). Drawing on impression management theory, we challenge this belief to argue that robot teacher confidentiality (i.e., robot teachers not sharing student interactions with the human teacher) lets students make more use of the technology. To examine this effect and provide deeper insights into multiple mechanisms and boundary conditions, we conduct six field, laboratory and online experiments that use virtual and physical robot teachers (Total N = 2,012). We first show that students indeed make more use of a confidential (vs. nonconfidential) robot teacher (both physical and virtual). In a qualitative study (Study 2), we use structural topic modeling to inductively identify relevant mediators and moderators. Studies 3 through 5 provide support for these, showing two key mediators (i.e., social judgment concern and interaction anxiety) and two moderators (i.e., student prevention focus and teacher benevolence) for the effect of robot teacher confidentiality. Collectively, the present research introduces the concept of service robot confidentiality, illustrating why and how not sharing HRI with a third actor critically impacts educational service encounters.

Capturing forceful interaction with deformable objects using a deep learning-powered stretchable tactile array

Capturing forceful interaction with deformable objects during manipulation benefits applications like virtual reality, telemedicine, and robotics. Replicating full hand-object states with complete geometry is challenging because of the occluded object deformations. Here, we report a visual-tactile recording and tracking system for manipulation featuring a stretchable tactile glove with 1152 force-sensing channels and a visual-tactile joint learning framework to estimate dynamic hand-object states during manipulation. To overcome the strain interference caused by contact with deformable objects, an active suppression method based on symmetric response detection and adaptive calibration is proposed and achieves 97.6% accuracy in force measurement, contributing to an improvement of 45.3%. The learning framework processes the visual-tactile sequence and reconstructs hand-object states. We experiment on 24 objects from 6 categories including both deformable and rigid ones with an average reconstruction error of 1.8 cm for all sequences, demonstrating a universal ability to replicate human knowledge in manipulating objects with varying degrees of deformability.

Robotics in Disaster Management: AI-Driven Rescue, Aerial Mapping, and Emerging IoT Applications for Effective Response

This review paper explores the growing role of robotics in disaster management, focusing on the integration of artificial intelligence (AI), Internet of Things (IoT) applications, and advanced simulation environments. Highlighting lessons from events like the Fukushima nuclear disaster and Wenchuan earthquake, it examines the deployment of aerial, ground, and underwater robots in tasks such as data collection, search and rescue, and hazard monitoring. AIoT frameworks enable these robotic systems to classify objects and make decisions on-site, supporting timely and efficient responses in hazardous conditions. Virtual platforms like the DARPA Virtual Robotics Challenge, using simulators such as Gazebo and CloudSim, facilitate realistic testing of robotic capabilities in disaster scenarios, advancing operational readiness through virtual training. This review synthesizes recent progress in AI-driven disaster robotics, identifies challenges, and discusses potential improvements for future applications.

A Robotic Teleoperation System with Integrated Augmented Reality and Digital Twin Technologies for Disassembling End-of-Life Batteries

Disassembly is a key step in remanufacturing, especially for end-of-life (EoL) products such as electric vehicle (EV) batteries, which are challenging to dismantle due to uncertainties in their condition and potential risks of fire, fumes, explosions, and electrical shock. To address these challenges, this paper presents a robotic teleoperation system that leverages augmented reality (AR) and digital twin (DT) technologies to enable a human operator to work away from the danger zone. By integrating AR and DTs, the system not only provides a real-time visual representation of the robot’s status but also enables remote control via gesture recognition. A bidirectional communication framework established within the system synchronises the virtual robot with its physical counterpart in an AR environment, which enhances the operator’s understanding of both the robot and task statuses. In the event of anomalies, the operator can interact with the virtual robot through intuitive gestures based on information displayed on the AR interface, thereby improving decision-making efficiency and operational safety. The application of this system is demonstrated through a case study involving the disassembly of a busbar from an EoL EV battery. Furthermore, the performance of the system in terms of task completion time and operator workload was evaluated and compared with that of AR-based control methods without informational cues and ‘smartpad’ controls. The findings indicate that the proposed system reduces operation time and enhances user experience, delivering its broad application potential in complex industrial settings.

Muscle‐Inspired Robust Anisotropic Cellulose Conductive Hydrogel for Multidirectional Strain Sensors and Implantable Bioelectronics

AbstractIntegrating superior mechanical performance, anisotropic conductivity, and biocompatibility into conductive hydrogels as all‐in‐one human‐machine interaction device remains challenging. Herein, by mimicking the anisotropic structures of human muscles, a robust anisotropic conductive hydrogel is developed by initially aligning polyvinyl alcohol with polypyrrole decorated cellulose nanofibrils to form an anisotropically oriented polymer networks, followed by post‐crosslinking with tannic acid (TA). Introducing TA into hydrogel network permanently secures its hierarchically anisotropic structure through multiple hydrogen bonds, thus endowing the hydrogel with exceptional mechanical properties (tensile strength of 11.41 MPa, toughness of 12.44 MJ m−3), anisotropic adhesive property, and direction‐dependent conductivity. With these attributes, a hydrogel strain sensor with excellent multidirectional sensitivity is developed, enabling stable monitoring of multi‐degrees of freedom joint movements in the human body and facilitating the control of a multiaxial virtual robot manipulator. Moreover, the in vitro/vivo tests demonstrate exceptional biocompatibility and anti‐biofouling properties of the as‐prepared hydrogel sensor, maintaining stable electronic response signals for over 14 days after successful implantation into the Achilles tendon of mice. Overall, this study presents a promising approach for designing conductive hydrogels with superior mechanical properties and anisotropic functionality for emerging applications in both in vitro and in vivo human‐machine interface materials.

A parallel mechanism-based virtual biomechanical shoulder robot model: Mechanism design optimization and motion planning

This paper presents the design and optimization process of a Virtual Biomechanical Shoulder Robot Model (VBSRM) based on a 6-4 parallel mechanism. To address the challenges posed by parallel manipulators and the specific biomechanical constraints of the shoulder joint, a comprehensive design analysis is conducted. First, a kinematic model of the VBSRM is developed, followed by an investigation of its geometric model. To obtain an optimal VBSRM design, performance objectives such as condition number, norm of actuator force, and stiffness are identified and optimized. Initially, only condition number of the robot mechanism is optimized using Genetic Algorithm and performance objectives from the optimal design are analyzed. Later, the three objectives are grouped to form a single function and a single objective-based optimization is also conducted. However, further investigation revealed the conflicting nature of the objectives and hence these were simultaneously optimized using the Non-dominated Sorting Genetic Algorithm (NSGA II). The results obtained from various optimization routines are compared and it is found that the results from the NSGA II provide a better tradeoff between the performance objectives. The motion trajectories from the optimal design of the VBSRM are later analyzed vis-à-vis human shoulder motions for its intended use as a robotic model of the human shoulder joint in various applications.

A Vision-based Robotic Navigation Method Using an Evolutionary and Fuzzy Q-Learning Approach

The paper presents a Fuzzy Q-Learning (FQL) and optical flow based autonomous navigation approach. The FQL method takes decisions in an unknown environment and without mapping, using motion information and through a reinforcement signal into an evolutionary algorithm. The reinforcement signal is calculated by estimating the optical flow densities in areas of the camera to determine whether they are “dense” or “thin” which has a relationship with the proximity of objects. The results obtained show that the present approach improves the rate of learning compared with a method with a simple reward system and without the evolutionary component. The proposed system was implemented in a virtual robotics system using the CoppeliaSim software and in communication with Python.

Motion capture and AR based programming by demonstration for industrial robots using handheld teaching device

In the industrial robots field, efficient and convenient programming methods have been a hot research topic. In recent years, immersive simulation technology has been developing rapidly in many fields, which provides new horizons for the development of industrial robots. This paper presents a HTC VIVE laser scan motion capture and Holohens augmented reality (AR) based interactive Programming by Demonstration (PbD) system for industrial robot. A portable Handheld Teaching Device (HTD) and its calibration algorithm are designed in the system. The portable HTD which is tracked by a laser motion capture system can be viewed as an AR robot end-effector to teach paths. Meanwhile, the AR robot can be simulated in real time during programing. In addition, the robot reproducing the operator’s actions at the same position in space is the focus of programming. So, Multi-system registration methods are proposed to determine the relationship between robot systems, motion capture systems and virtual robot systems. Meanwhile, a path planning algorithm is proposed to convert the captured raw path points into robot-executable code. For unskilled operators, they can easily perform complex programming using the HTD. For skilled senior workers, their skills can be quickly learned by robots using the system.

3D dynamics and control of a snake robot in uncertain underwater environment

Abstract The snake robot can be used to monitor and maintain underwater structures and environments. The motion of a snake robot is achieved by lateral undulation which is called the gait pattern of the snake robot. The parameters of a gait pattern need to be adjusted for compensating environmental uncertainties. In this work, 3D motion dynamics of a snake robot for the underwater environment is proposed with vertical motion using the buoyancy variation technique and horizontal motion using lateral undulation. “The neutral buoyant snake robot motion in hypothetical plane and added mass effect is negligible”, these previous assumptions are removed in this work. Two different control algorithms are designed for horizontal and vertical motions. The existing super twisting sliding mode control (STSMC) is used for the horizontal serpentine motion of the snake robot. The control law is designed on a reduced-ordered dynamic system based on virtual holonomic constraints. The vertical motion is achieved by controlling the mass variation using a pump. The water pumps are controlled using the event-based controller or Proportional Derivative (PD) controller. The results of the proposed control technique are verified with various external environmental disturbances and uncertainties to check the robustness of the control approach for various path following cases. Moreover, the results of STSMC scheme are compared with SMC scheme to check the effectiveness of STSMC. The practical implementation of the work is also performed using Simscape Multibody environment where the designed control algorithm is deployed on the virtual snake robot.

Application of Artificial Neuromolecular System in Robotic Arm Control to Assist Progressive Rehabilitation for Upper Extremity Stroke Patients

Freedom of movement of the hands is the most desired hope of stroke patients. However, stroke recovery is a long, long road for many patients. If artificial intelligence can assist human arm movement, the possibility of stroke patients returning to normal hand movement might be significantly increased. This study uses the artificial neuromolecular system (ANM system) developed in our laboratory as the core of motion control, in an attempt to learn to control the mechanical arm to produce actions similar to human rehabilitation training and the transition between different activities. This research adopts two methods. The first is hypothetical exploration, the so-called “artificial world” simulation method. The detailed approach uses the V-REP (Virtual Robot Experimentation Platform) to conduct different experimental runs to capture relevant data. Our policy is to establish an action database systematically to a certain extent. From these data, we use the ANM system with self-organization and learning capabilities to develop the relationship between these actions and establish the possibility of conversion between different activities. The second method of this study is to use the data from a hospital in Toronto, Canada. Our experimental results show that the ANM system can continuously learn for problem-solving. In addition, our three experimental results of adaptive learning, transfer learning, and cross-task learning further confirm that the ANM system can use previously learned systems to complete the delivered tasks through autonomous learning (instead of learning from scratch).

The accounting of virtual robots

Purpose: The aim of the article is to present the principles and methods of measuring the activity of virtual robots. We link the identified metrics to analogies of perceiving a bot as a machine, an automated process, or a digital employee. The latest phenomenon of the anthropomorphization of bots receives special attention.Methodology/approach: We used text analysis of technical and marketing documentation as the research method. We conducted the analysis as a case study using the software of a leading international RPA (Robotic Process Automation) provider.Findings: The results lead to the following conclusions: accounting using bots is multidimensional, embedded in technology, and carried out in real time. The virtual robot accounting solutions avoid the analogy of virtual employees; measurement covers both bots and operators, and the representation of activities is incomplete and distorted.Research limitations/implications: The research was focused on subjective recommendations of the RPA technology provider, taking into account their marketing goals. Originality/value: The research shows that embedded measurement technology automates raw data collection and enables real-time accounting for both bots and their operators. It thus contributes to the development of hybrid accounting. Simultaneously, this technology has a constraining effect, distorting measurement and redefining well-known and well-established effectiveness metrics.

Overtrust in AI Recommendations About Whether or Not to Kill: Evidence from Two Human-Robot Interaction Studies

This research explores prospective determinants of trust in the recommendations of artificial agents regarding decisions to kill, using a novel visual challenge paradigm simulating threat-identification (enemy combatants vs. civilians) under uncertainty. In Experiment 1, we compared trust in the advice of a physically embodied versus screen-mediated anthropomorphic robot, observing no effects of embodiment; in Experiment 2, we manipulated the relative anthropomorphism of virtual robots, observing modestly greater trust in the most anthropomorphic agent relative to the least. Across studies, when any version of the agent randomly disagreed, participants reversed their threat-identifications and decisions to kill in the majority of cases, substantially degrading their initial performance. Participants’ subjective confidence in their decisions tracked whether the agent (dis)agreed, while both decision-reversals and confidence were moderated by appraisals of the agent’s intelligence. The overall findings indicate a strong propensity to overtrust unreliable AI in life-or-death decisions made under uncertainty.

Enhancing executive functions in children: a comprehensive review of interventions via digital technologies and future directions

This review critically examines a broad spectrum of interventions aimed at bolstering executive functions (EFs) in children, a cornerstone for their academic, social, and cognitive development. We delve into a variety of EF enhancement strategies from June 2006 to March 2024, encompassing computer-based programs, physical exercises, classroom adjustments, and innovative technologies like virtual reality and educational robotics. Stressing the importance of parental involvement and positive teacher-student interactions, the review proposes an evidence-based approach tailored to individual needs, offering significant benefits particularly to children with or at risk for conditions such as ADHD and ASD. The synthesis underscores the imperative of collaborative initiatives among educators, policymakers, and families for fostering inclusive educational practices that nurture holistic EF development. The review sets forth the need for future research to embark on longitudinal studies and explore novel technologies, aiming to refine evidence-based educational strategies and enhance the cognitive and emotional well-being of children globally.

Anthropomorphism-based artificial intelligence (AI) robots typology in hospitality and tourism

Purpose Anthropomorphism plays a crucial role in the deployment of human-like robots in hospitality and tourism. This study aims to propose an anthropomorphism-based typology of artificial intelligence (AI) robots, based on robot attributes, usage, function and application across different operational levels. Design/methodology/approach Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) checklist, the research was conducted in two stages. A search strategy was implemented to explore anthropomorphism-based AI robots and to develop a robot typology. Findings This study provides a comprehensive typology of anthropomorphism-based AI robots used in tourism and hospitality and classifies them into four types, namely, chatbots, mechanoids, humanoids and android robots. Each type features distinct functions and applications. Practical implications The findings can assist companies in using anthropomorphic robots to improve service and strengthen competitiveness. This study offers valuable insights to managers for deploying AI robots across diverse service sectors. Originality/value This research provides a novel typology of hospitality and tourism AI robots and extends the understanding of anthropomorphism in human–robot interaction. This typology encompasses both virtual and physical robots, providing clarity on their attributes, usage, functions and applications across diverse areas of hospitality operations.

Application of gamification based virtual robots in urban landscape Design: Interaction and entertainment experience in the design process

The traditional design process lacks fun and participation, so new elements need to be introduced to enhance the attraction and creativity of the design. The goal of the research is to design an urban landscape design method based on gamified elements and virtual robots to increase the interactivity and entertainment experience in the design process, and to explore its impact on the design results. This paper proposes an urban landscape design framework based on gamified elements and virtual robots, which includes multiple stages in the design process, each of which introduces different gamified tasks and interactions of virtual robots. Designers can gain new design ideas and inspiration by completing tasks and interacting with virtual robots. The study evaluated the effects of design methods using gamification elements and virtual robots on the design process and design results. The results show that this approach effectively increases the engagement and enjoyment of the design process, while also promoting innovation and sustainability of the design results. The urban landscape design method based on gamification elements and virtual robots has broad application prospects, which can bring more creativity and fun to urban landscape design.

IMPLEMENTASI TEKNIK SMOTE UNTUK MENGATASI IMBALANCE CLASS DALAM KLASIFIKASI SENTIMEN MENGENAI CHATGPT

ChatGPT is a chatbot or computer program in the form of a virtual robot that can simulate human-like conversations. ChatGPT is widely used in various fields in academia. The impact of the use of ChatGPT on academia and public perception of this technology is significant. Sentiment analysis can be used to determine the polarity of a text or opinion that is positive or negative. In this research, social media is used as a data source to collect public opinion regarding ChatGPT instantly. The methods used in this reserach are the KNN algorithm and Naive Bayes algorithm. The aim of this research is to find the best algorithm model for sentiment classification in terms of public opinion for ChatGPT which contains English text. Before testing the algorithm model, a text processing stage was carried out which included the processes of case folding, tokenizing, stopword removal, and stemming. Word weighting using TF-IDF was carried out before the data was ready to be processed. Splitting data used in this research includes 80% of the dataset as training data and 20% of the dataset as testing data. The application of the SMOTE technique to the KNN and Naive Bayes algorithms to overcome the imbalance class of the public opinion dataset regarding ChatGPT. The research results show that combining SMOTE and Naive Bayes algorithm gives the best results with an accuracy value of 85.00%, a precision value of 87.64%, a recall value of 84.78% and an f1-score of 86.18%.

Simulation of street landscape design based on machine learning and entertainment design robots: An interactive entertainment design experience

Traditional design methods usually present design ideas through plans and models, but this method can not truly simulate the design effect. Therefore, this research aims to use computer game simulation and virtual robotics technology to provide a more interactive and realistic design experience. The study creates a virtual street environment with elements such as streets, buildings and vegetation through computer game simulation. Participants can move and interact in this environment through virtual robots, experience the process of street landscape design, adjust the street layout, add landscape elements, and observe the design effect in real time according to their own design intentions. Compared with traditional design methods, the experimental results show that the design experience using computer game simulation and virtual robot technology is more vivid, creative and interactive. Participants can better perceive the design effect and adjust the design scheme in real time. This interactive entertainment design experience method can help designers and participants better understand and evaluate the design scheme, and improve the design effect and participation.

Developing a tablet-based brain-computer interface and robotic prototype for upper limb rehabilitation.

The current study explores the integration of a motor imagery (MI)-based BCI system with robotic rehabilitation designed for upper limb function recovery in stroke patients. We developed a tablet deployable BCI control of the virtual iTbot for ease of use. Twelve right-handed healthy adults participated in this study, which involved a novel BCI training approach incorporating tactile vibration stimulation during MI tasks. The experiment utilized EEG signals captured via a gel-free cap, processed through various stages including signal verification, training, and testing. The training involved MI tasks with concurrent vibrotactile stimulation, utilizing common spatial pattern (CSP) training and linear discriminant analysis (LDA) for signal classification. The testing stage introduced a real-time feedback system and a virtual game environment where participants controlled a virtual iTbot robot. Results showed varying accuracies in motor intention detection across participants, with an average true positive rate of 63.33% in classifying MI signals. The study highlights the potential of MI-based BCI in robotic rehabilitation, particularly in terms of engagement and personalization. The findings underscore the feasibility of BCI technology in rehabilitation and its potential use for stroke survivors with upper limb dysfunctions.

Research on virtual entertainment robots based on machine learning algorithms providing psychological health services for college students

In modern society, college students are facing increasing psychological pressure and mental health problems. In this context, virtual entertainment robots have become a promising form of mental health services, which can utilize machine learning algorithms to provide personalized psychological support and guidance by analyzing a large amount of psychological data and user information. Study the use of sample calculation and screening methods to determine the number of samples and perform feature selection to improve algorithm performance. Then analyze the detection effect and evaluate the effectiveness of the algorithm. By designing the architecture of a virtual entertainment robot and adopting anti-interference strategies to ensure that the robot can accurately recognize mental health information, text recognition technology was implemented, its effectiveness was evaluated, and further multi-source information recognition was carried out to improve recognition accuracy. Finally, a psychological health evaluation system for college students was constructed, and corresponding psychological health service strategies were proposed to meet the needs of college students. The results of this study indicate that virtual entertainment robots based on machine learning algorithms can effectively provide mental health services, providing support and guidance for the mental health problems of college students.