Robot Communication Research Articles

Gradual Rhythm Change of a Drumming Robot Enhances the Pseudosense of Leading in Human–Robot Interactions

In order to achieve symbiosis between humans and social robots, it is not enough to simply design a robot with excellent functionality, but it is necessary to give humans a friendly impression of the robot. Rhythmic synchronization is the foundation of social interaction and unity, and it is expected that research on synchronization between humans and robot will contribute to the design theory of robot that can build intimate relationships with humans. Specifically, the sense of leading a rhythm evokes a pleasant emotion. If a communication robot is behaviorally synchronized with humans, a strong sense of unity with the robot could occur in keeping with their sense of leading. However, in rhythmic interactions, having the sense of leading the rhythm often differs from the actual rhythm leading. We conducted three kinds of experiments to examine whether a pseudosense of leading occurred in human-robot or human-human drumming interactions. At the experiment 1, we used an interaction task of human-robot drumming to examine whether participants experienced a strong sense of leading when interacting with a robot drumming with gradual rhythm fluctuations and whether the sense of leading the rhythm induced a positive impression of the robot. As a result, we found that a pseudosense of leading the rhythm and the pleasant emotion of drumming occurred even from the artificial robot’s rhythm, notably when the rhythm of the robot changed gradually. At the experiment 2, we also showed that the pseudosense of leading was not likely to occur in the drumming between humans. Finally, at the experiment 3, we found that the pseudosense of leading would induce anthropomorphic impressions of the robots. These findings may contribute to designing interactive robots’ rhythmic behavior to realize symbiosis between humans and robots.

Communication Robots and Potential for Clinical Applications

Communication Robots and Potential for Clinical Applications

Modeling Salesclerks’ Utterances in Bespoke Scenes and Experimental Evaluation of It Using a Communication Robot

Modeling Salesclerks’ Utterances in Bespoke Scenes and Experimental Evaluation of It Using a Communication Robot

The 30th IEEE/RSJ International Conference on Robot and Human Interactive Communication (RoMAN 2021)

The 30th IEEE/RSJ International Conference on Robot and Human Interactive Communication (RoMAN 2021)

Development of a Heart Rate Measurement System to Investigate The Influence of Communication Robot Behavior on Users’ Emotions

Development of a Heart Rate Measurement System to Investigate The Influence of Communication Robot Behavior on Users’ Emotions

Human–Robot Communication System for an Isolated Environment

Human–Robot Communication System for an Isolated Environment

Train here, drive there: ROS based end-to-end autonomous-driving pipeline validation in CARLA simulator using the NHTSA typology

Urban complex scenarios are the most challenging situations in the field of Autonomous Driving (AD). In that sense, an AD pipeline should be tested in countless environments and scenarios, escalating the cost and development time exponentially with a physical approach. In this paper we present a validation of our fully-autonomous driving architecture using the NHTSA (National Highway Traffic Safety Administration) protocol in the CARLA simulator, focusing on the analysis of our decision-making module, based on Hierarchical Interpreted Binary Petri Nets (HIBPN). First, the paper states the importance of using hyper-realistic simulators, as a preliminary help to real test, as well as an appropriate design of the traffic scenarios as the two current keys to build safe and robust AD technology. Second, our pipeline is introduced, which exploits the concepts of standard communication in robotics using the Robot Operating System (ROS) and the Docker approach to provide the system with isolation, flexibility and portability, describing the main modules and approaches to perform the navigation. Third, the CARLA simulator is described, outlining the steps carried out to merge our architecture with the simulator and the advantages to create ad-hoc driving scenarios for use cases validation instead of just modular evaluation. Finally, the architecture is validated using some challenging driving scenarios such as Pedestrian Crossing, Stop, Adaptive Cruise Control (ACC) and Unexpected Pedestrian. Some qualitative (video files: Simulation Use Cases) and quantitative (linear velocity and trajectory splitted in the corresponding HIBPN states) results are presented for each use case, as well as an analysis of the temporal graphs associated to the Vulnerable Road Users (VRU) cases, validating our architecture in simulation as a preliminary stage before implementing it in our real autonomous electric car.

Active Inference Through Energy Minimization in Multimodal Affective Human-Robot Interaction.

During communication, humans express their emotional states using various modalities (e.g., facial expressions and gestures), and they estimate the emotional states of others by paying attention to multimodal signals. To ensure that a communication robot with limited resources can pay attention to such multimodal signals, the main challenge involves selecting the most effective modalities among those expressed. In this study, we propose an active perception method that involves selecting the most informative modalities using a criterion based on energy minimization. This energy-based model can learn the probability of the network state using energy values, whereby a lower energy value represents a higher probability of the state. A multimodal deep belief network, which is an energy-based model, was employed to represent the relationships between the emotional states and multimodal sensory signals. Compared to other active perception methods, the proposed approach demonstrated improved accuracy using limited information in several contexts associated with affective human–robot interaction. We present the differences and advantages of our method compared to other methods through mathematical formulations using, for example, information gain as a criterion. Further, we evaluate performance of our method, as pertains to active inference, which is based on the free energy principle. Consequently, we establish that our method demonstrated superior performance in tasks associated with mutually correlated multimodal information.

The rise of the machines and how they impact workers' psychological health: An empirical study

Artificial intelligence (AI) will fundamentally change the global labor market in the near future. While many papers have been published on related topics—such as algorithm development, human–robot communication, laws, and legislation surrounding AI—the psychological impacts of its implementation in workplaces are still largely unknown. Furthermore, existing theoretical models of AI effects do not incorporate psychological outcomes, which translates into a major gap in the scientific literature. Therefore, this study's purpose was to empirically verify a newly developed theoretical model that integrates one individual determinant (e.g., general mental ability [GMA]) and the psychological outcomes of AI in the workplace (e.g., the effects of AI perceived usefulness on the basic psychological needs and psychological health). To that end, five hundred forty-six employees (46.7% women and 53.3% men) working at least 20 hr per week in various Canadian organizations answered several online questionnaires. Collected data were analyzed using path analysis. First, the results of this study showed that the GMA-level influences AI perceived usefulness-level, which in turn determines its positive or negative perception. This perception differentially impacts employees' satisfaction or thwarting of basic psychological needs and also indirectly their psychological health. By combining three different theoretical approaches, this study contributes to connecting interdisciplinary knowledge of AI impacts on workers' psychological health and to reducing gaps between related research fields.

How Do Traditional Media Function in Social Learning about AI? Psychological and Cognitive Reactions to AI-Powered Communication

ABSTRACT This exploratory study examines how young Japanese people’s emotion, risk-benefit perception, and interaction intention toward AI-powered communication robots are related to their media exposure. Analyses were based on data collected from a sample of 141 Japanese college students. Findings suggest that reading newspapers can foster positive emotion, which tends to be not only associated with higher perceived benefits and lower perceived risk but also have the potential to increase intentions to interact with AI-powered communication robots; however, watching television can contribute directly to reduced interaction intentions. Using Internet-based media seems to play no essential role in motivating changes in reactions. Findings of this study shed light on the public acceptance of AI technology and have practical implications for facilitating the application of AI technology in everyday life.

A commentary of Multi-skilled AI in MIT Technology Review 2021

Towards the end of 2012, artificial intelligence (AI) scientists first figured out how to impart “vision” to neural networks. Later, they also mastered how to enable neural networks to mimic human reasoning, hearing, speaking, and writing. Although AI has become similar to or even superior to humans in accomplishing specific tasks, it still does not possess the “flexibility” of the human brain, i.e., the human brain can apply skills learned in one situation to another.Taking cues from the growth process of children, we think about the following question. If senses and language can be combined, and AI can perform at a level closer to humans in terms of collecting and processing information, will it be able to develop an understanding of the world? The answer is yes. “Multi-modal” systems, which can simultaneously acquire human senses and language, thereby generating significantly stronger AI, and making it easier for AI to adapt to new situations and solve new problems. Hence, such algorithms can be used to solve more complex problems, or be implanted into robots for communication and collaboration with humans in our daily lives. In September 2020, researchers from the Allen Institute for AI (AI2) created a model that could generate images from captions, thus demonstrating the ability of the algorithm to associate words with visual information. In November, scientists from the University of North Carolina at Chapel Hill developed a method of incorporating images into existing language models, which significantly enhanced the ability of the model to comprehend text. Early in 2021, OpenAI extended GPT-3 and released two visual language models: one associates the objects in the image with the words in the descriptions, and another one generates a digital image based on the combination of concepts it has learned. The progress made by “multi-modal” systems, in the long run, will help break through the limits of AI. It will not only unlock new AI applications, but also make these applications safer and more reliable. More sophisticated multi-modal systems will also aid the development of more advanced robot assistants. Ultimately, multi-modal systems may prove to be the first AI that we can trust.①①Original source in Chinese: R. Ji, Multi-skilled AI, Bulletin of National Natural Science Foundation of China. 35 (3) (2021) 413-415.

Design of Gel Fiber Turnover for Self Cleaning in Soft Robotics

Human-robot interaction in soft robots involves physical touch with the user. Soft matter materials such as silicone and urethane are often used in the bodies of soft robots. These materials have advantages excellent softness and waterproofness, however the tackiness of the surface causes solid dust and lipophilic stains to adhere. In order to realize long-term operation of the soft robot for communication, a method of preventing external stains is required. Coating, which is one of the methods, is not recommended because it impairs the texture of soft materials.Therefore, we focus on hydrogels which have excellent water content and adsorptivity. The stains are classified into three types: water-soluble stains, lipophilic stains, and solid stains. Hydrogel absorbs water-based stains. Moisture inside can be expected to have a oil-repellent function that prevents the invasion of lipophilic stains. Then, it is also possible to impart electrical properties and adsorb solid dust by changing the monomer contained in the gel to anionic or cationic.On the other hand, although these gel materials have excellent functionality for preventing stains, there are problems that water evaporates and shrinks, and that the caught stains remain on the surface or inside. In order to solve this problem, we introduce a gel fiber system. This system forms the gel fiber-covered surface and adsorbs external dust. In this study, the stain removing performance of the fibrous gel on the surface is quantified using image processing.

Construction of multi-modal perception model of communicative robot in non-structural cyber physical system environment based on optimized BT-SVM model

With the rapid development of intelligent control technology, computer technology, bionics, artificial intelligence and other disciplines, more and more attention has been paid to the research of intelligent mobile robot technology, and autonomous positioning is the basis for mobile robots to conduct autonomous navigation and exploration research. Sensors assist each other to provide a wealth of perception information about the internal state of the robot and the external surrounding environment. This paper proposes a method for optimizing the Support Vector Machine (SVM) multi-classifier with a binary tree structure, which improves the accuracy of multi-modal tactile signal recognition. The improved particle swarm clustering algorithm is used to optimize the binary tree structure, reduce the error accumulation of the binary tree structure SVM multi-classifier, and further improve the accuracy of multi-modal tactile signal recognition. The effect of the method in this paper is verified by robot grasping experiments. The results show that the use of multi-modal information of two-dimensional images and three-dimensional point cloud images can effectively identify and locate target objects of different shapes. Compared with the processing method of two-dimensional or point cloud monomodal image information, the positioning error can be reduced by 54.8%, and the direction error can be reduced by 50.8%, which has better robustness and accuracy. The simulation results show that the improved PSOBT-SVM model has the best classification effect for artificial features, PCA features and spatio-temporal correlation features. The improved PSOBT-SVM optimizes the classification accuracy without changing the number of SVM classifiers, and proves its accuracy in classifying multimodal tactile signals.

Robotic Communications for 5G and Beyond: Challenges and Research Opportunities

The ongoing surge in applications of robotics brings both opportunities and challenges for the fifth-generation (5G) and beyond (B5G) of communication networks. This article focuses on 5G/B5G-enabled terrestrial robotic communications with an emphasis on distinct characteristics of such communications. First, signal and spatial modeling for robotic communications are presented. To elaborate further, both the benefits and challenges derived from robots' mobility are discussed. As a further advance, a novel simultaneous localization and radio mapping (SLARM) framework is proposed for integrating localization and communications into robotic networks. Furthermore, dynamic trajectory design and resource allocation for both indoor and outdoor robots are provided to verify the performance of robotic communications in the context of typical robotic application scenarios.

Voice Recognition to Control and Monitor Robotic Arm on IoT by Using Smartphone

Robotic control approaches are extensively studied and developed in recent years, especially in industrial manufacturing. They are explored in various way from using image-processing to voice-controlled application. Robot communication techniques are required to develop by three dimensions of production: better quality connection; safer and cheaper. In this context, this paper proposes a protocol of control and monitor Robotics by using a developed technology of the 4.0 industries: Internet of Thing (IoT). Control system by voice recognition using Google Assistant on Smartphone and IoT communication by using Adafruit IO with IFTTT platform as data transfer intermediaries. Our approach applies to control an own developed robotic arm with Arduino Uno as the controller. Our paper show also the voice controlling commands are successfully transmitted via IoT technology and the desired operations are performed successfully and also analyse its advantages and disadvantages and the perspectives.

Effects of LED on Emotion-Like Feedback of a Single-Eyed Spherical Robot

Non-verbal communication is important in human interaction. It provides a layer of information that complements the message being transmitted. This type of information is not limited to human speakers. In human- robot communication, increasing the animacy of the robotic agent―by using non-verbal cues―can aid the expression of abstract concepts such as emotions. Considering the physical limitations of artificial agents, robots can use light and movement to express equivalent emotional feedback. This study analyzes the effects of LED and motion animation of a spherical robot on the emotion being expressed by the robot. A within-subjects experiment was conducted at the University of Tsukuba where participants were asked to rate 28 video samples of a robot interacting with a person. The robot displayed different motions with and without light animations. The results indicated that adding LED animations changes the emotional impression of the robot for valence, arousal, and dominance dimensions. Furthermore, people associated various situations according to the robot’s behavior. These stimuli can be used to modulate the intensity of the emotion being expressed and enhance the interaction experience. This paper facilitates the possibility of designing more affective robots in the future, using simple feedback.

A review on communication protocols for autonomous unmanned aerial vehicles for inspection application

The communication system is a critical part of the system design for the autonomous Unmanned Aerial Vehicle (UAV). It has to address different considerations, including efficiency, reliability and mobility of the UAV. In addition, a multi-UAV system requires a communication system to assist information sharing, task allocation and collaboration in a team of UAVs. In this paper, we review communication solutions for supporting a team of UAVs while considering an application in the power line inspection industry. We provide a review of candidate wireless communication technologies for supporting communication in UAV applications. Performance measurements and UAV-related channel modeling of those candidate technologies are reviewed. A discussion of current technologies for building UAV mesh networks is presented. We then analyze the structure, interface and performance of robotic communication middleware, ROS and ROS2. Based on our review, the features and dependencies of candidate solutions in each layer of the communication system are presented.

Research and application of substation cable trench inspection robot communication system

The substation cable trench inspection robot is mainly used to inspect the high-voltage cables in the underground cable trench of the substation, so as to find various hidden dangers in time. However, due to serious electromagnetic interference in the cable trench and no communication network underground, the environment is more complicated. Aiming at the complex environment in the cable trench, this paper designs a WLAN-based cable trench inspection robot communication program, combining WLAN with directional antennas, and relaying through relay routing, so that the communication between the robot and the PC is divided into pipeline communication and ground communication Two parts. At the same time, the communication hardware, software, and protocol are designed based on the B/S architecture, which effectively improves the communication distance. Through field verification, the packet loss rate of the communication system proposed in the thesis is less than 0.5‰, the communication is stable, and the communication task of the robot in the cable trench of the substation can be completed well.

Design and Implementation of a Wireless Medical Robot for Communication Within Hazardous Environments.

The huge spreading of COVID-19 viral outbreak to several countries motivates many of the research institutions everywhere in numerous disciplines to try decreasing the spread rate of this pandemic. Among these researches are the robotics with different payloads and sensory devices with wireless communications to remotely track patients’ diagnosis and their treatment. That is, it reduces direct contact between the patients and the medical team members. Thus, this paper is devoted to design and implement a prototype of wireless medical robot (MR) that can communicate between patients and medical consultants. The prototype includes the modelling of a four-wheeled MR using systems' identification methodology, from which the model is utilized in control design and analysis. The required controller is designed using the proportional-integral-derivative (PID) and Fuzzy logic (FLC) techniques. The MR is equipped onboard with some medical sensors and a camera to acquire vital signs and physical parameters of patients. The MR model is obtained via an experimental test with input/output signals in open-loop configuration as single–input–single–output from which the estimation and validation results demonstrate that the identified model possess about 89% of the output variation/dynamics. This model is used for controllers' design with PID and FLC, the response of which is good for heading angle tracking. Concerning the medical measurements, more than two thousand real recorded Photo-plethysmography (PPG) signals and Blood Pressure (BP) are used to find the appropriate BP estimation model. Towards this objective, some experiments are designed and conducted to measure the PPG signal. Finally, the BP is estimated with mean absolute error of about 4.7 mmHg in systolic and 4.8 mmHg in diastolic using Artificial Neural Network.

Robot Transparency and Anthropomorphic Attribute Effects on Human-Robot Interactions.

Anthropomorphic robots need to maintain effective and emotive communication with humans as automotive agents to establish and maintain effective human–robot performances and positive human experiences. Previous research has shown that the characteristics of robot communication positively affect human–robot interaction outcomes such as usability, trust, workload, and performance. In this study, we investigated the characteristics of transparency and anthropomorphism in robotic dual-channel communication, encompassing the voice channel (low or high, increasing the amount of information provided by textual information) and the visual channel (low or high, increasing the amount of information provided by expressive information). The results showed the benefits and limitations of increasing the transparency and anthropomorphism, demonstrating the significance of the careful implementation of transparency methods. The limitations and future directions are discussed.