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Intelligent Human-Robot Interaction

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A special issue of Biomimetics (ISSN 2313-7673). This special issue belongs to the section "Locomotion and Bioinspired Robotics".

Deadline for manuscript submissions: closed (20 August 2023) | Viewed by 24668

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Special Issue Editors

Prof. Dr. Yisheng Guan

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Guest Editor

School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China
Interests: bionic robotics; motion planning; automation and robotics; mechatronics robot motion planning; cognitive robotics; space robotics
Special Issues, Collections and Topics in MDPI journals

Dr. Weiwei Wan

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Guest Editor

School of Engineering Science, Osaka University, Osaka 565-0871, Japan
Interests: robot manipulation; motion planning; intelligent robot
Special Issues, Collections and Topics in MDPI journals

Dr. Li He

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Guest Editor

Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Interests: SLAM; machine learning; mobile robot; LiDAR; autonomous driving

Special Issue Information

Dear Colleagues,

Human–robot interaction (HRI) is a multi-disciplinary field that encompasses artificial intelligence, robotics, human–computer interaction, machine vision, natural language understanding, and social science. With the rapid development of AI and robotics, intelligent HRI has become an increasingly attractive issue in the field of robotics.

Intelligent HRI involves many challenges in science and technology, particularly in human-centered aspects. These include human expectations of, attitudes towards, and perceptions of robots, safety, acceptability, and comfortability of robotic behaviors, and the closeness of robots to humans. On the other hand, it is desired that robots can understand the attention, intention, and even emotion of humans, and make prompt corresponding responses, under the support of AI. Achieving excellent intelligent HRI requires R&D in this multi- and cross-disciplinary field, with efforts expected in all relevant aspects including actuation, sensing, perception, control, recognition, planning, learning, AI algorithms, intelligent IO, integrated systems, and so on.

The aim of this Special Issue is to reveal new concepts, ideas, findings, and the latest achievements in both theoretical research and technical development in intelligent HRI. We invite scientists and engineers from robotics, AI, computer science, and other relevant disciplines to present the latest results of their research and development in the field of intelligent HRI. The topics of interest include, but are not limited to, the following:

Intelligent sensors and systems;
Bio-inspired sensing and learning;
Multi-modal perception and recognition;
Social robotics;
Autonomous behaviors of robots;
AI algorithms in robotics;
Collaboration between humans and robots;
Advances and future challenges of HRI.

Prof. Dr. Yisheng Guan
Dr. Weiwei Wan
Dr. Li He
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

intelligent sensors and systems
bio-inspired sensing and learning
multi-modal perception and recognition
social robotics
autonomous behaviors of robots
AI algorithms in robotics
collaboration between humans and robots
advances and future challenges of HRI

Published Papers (10 papers)

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Research

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35 pages, 9958 KiB

Open AccessArticle

Examining the Impact of Digital Human Gaze Expressions on Engagement Induction

by Subin Mok, Sung Park and Mincheol Whang

Biomimetics 2023, 8(8), 610; https://doi.org/10.3390/biomimetics8080610 - 14 Dec 2023

Viewed by 1281

Abstract

With advancements in technology, digital humans are becoming increasingly sophisticated, with their application scope widening to include interactions with real people. However, research on expressions that facilitate natural engagement in interactions between real people and digital humans is scarce. With this study, we aimed to examine the differences in user engagement as measured by subjective evaluations, eye tracking, and electroencephalogram (EEG) responses relative to different gaze expressions in various conversational contexts. Conversational situations were categorized as face-to-face, face-to-video, and digital human interactions, with gaze expressions segmented into eye contact and gaze avoidance. Story stimuli incorporating twelve sentences verified to elicit positive and negative emotional responses were employed in the experiments after validation. A total of 45 participants (31 females and 14 males) underwent stimulation through positive and negative stories while exhibiting eye contact or gaze avoidance under each of the three conversational conditions. Engagement was assessed using subjective evaluation metrics in conjunction with measures of the subjects’ gaze and brainwave activity. The findings revealed engagement disparities between the face-to-face and digital-human conversation conditions. Notably, only positive stimuli elicited variations in engagement based on gaze expression across different conversation conditions. Gaze analysis corroborated the engagement differences, aligning with prior research on social sensitivity, but only in response to positive stimuli. This research departs from traditional studies of un-natural interactions with digital humans, focusing instead on interactions with digital humans designed to mimic the appearance of real humans. This study demonstrates the potential for gaze expression to induce engagement, regardless of the human or digital nature of the conversational dyads. Full article

(This article belongs to the Special Issue Intelligent Human-Robot Interaction)

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24 pages, 11830 KiB

Open AccessArticle

Probabilistic Dual-Space Fusion for Real-Time Human-Robot Interaction

by Yihui Li, Jiajun Wu, Xiaohan Chen and Yisheng Guan

Biomimetics 2023, 8(6), 497; https://doi.org/10.3390/biomimetics8060497 - 19 Oct 2023

Viewed by 1128

Abstract

For robots in human environments, learning complex and demanding interaction skills from humans and responding quickly to human motions are highly desirable. A common challenge for interaction tasks is that the robot has to satisfy both the task space and the joint space constraints on its motion trajectories in real time. Few studies have addressed the issue of hyperspace constraints in human-robot interaction, whereas researchers have investigated it in robot imitation learning. In this work, we propose a method of dual-space feature fusion to enhance the accuracy of the inferred trajectories in both task space and joint space; then, we introduce a linear mapping operator (LMO) to map the inferred task space trajectory to a joint space trajectory. Finally, we combine the dual-space fusion, LMO, and phase estimation into a unified probabilistic framework. We evaluate our dual-space feature fusion capability and real-time performance in the task of a robot following a human-handheld object and a ball-hitting experiment. Our inference accuracy in both task space and joint space is superior to standard Interaction Primitives (IP) which only use single-space inference (by more than 33%); the inference accuracy of the second order LMO is comparable to the kinematic-based mapping method, and the computation time of our unified inference framework is reduced by 54.87% relative to the comparison method. Full article

(This article belongs to the Special Issue Intelligent Human-Robot Interaction)

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18 pages, 6155 KiB

Open AccessArticle

Human Operation Augmentation through Wearable Robotic Limb Integrated with Mixed Reality Device

by Hongwei Jing, Tianjiao Zheng, Qinghua Zhang, Kerui Sun, Lele Li, Mingzhu Lai, Jie Zhao and Yanhe Zhu

Biomimetics 2023, 8(6), 479; https://doi.org/10.3390/biomimetics8060479 - 08 Oct 2023

Viewed by 1471

Abstract

Mixed reality technology can give humans an intuitive visual experience, and combined with the multi-source information of the human body, it can provide a comfortable human–robot interaction experience. This paper applies a mixed reality device (Hololens2) to provide interactive communication between the wearer and the wearable robotic limb (supernumerary robotic limb, SRL). Hololens2 can obtain human body information, including eye gaze, hand gestures, voice input, etc. It can also provide feedback information to the wearer through augmented reality and audio output, which is the communication bridge needed in human–robot interaction. Implementing a wearable robotic arm integrated with HoloLens2 is proposed to augment the wearer’s capabilities. Taking two typical practical tasks of cable installation and electrical connector soldering in aircraft manufacturing as examples, the task models and interaction scheme are designed. Finally, human augmentation is evaluated in terms of task completion time statistics. Full article

(This article belongs to the Special Issue Intelligent Human-Robot Interaction)

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30 pages, 7881 KiB

Open AccessArticle

Assessing Feasibility of Cognitive Impairment Testing Using Social Robotic Technology Augmented with Affective Computing and Emotional State Detection Systems

by Sergio Russo, Letizia Lorusso, Grazia D’Onofrio, Filomena Ciccone, Michele Tritto, Sergio Nocco, Daniela Cardone, David Perpetuini, Marco Lombardo, Daniele Lombardo, Daniele Sancarlo, Antonio Greco, Arcangelo Merla and Francesco Giuliani

Biomimetics 2023, 8(6), 475; https://doi.org/10.3390/biomimetics8060475 - 06 Oct 2023

Cited by 5 | Viewed by 1475

Abstract

Social robots represent a valid opportunity to manage the diagnosis, treatment, care, and support of older people with dementia. The aim of this study is to validate the Mini-Mental State Examination (MMSE) test administered by the Pepper robot equipped with systems to detect psychophysical and emotional states in older patients. Our main result is that the Pepper robot is capable of administering the MMSE and that cognitive status is not a determinant in the effective use of a social robot. People with mild cognitive impairment appreciate the robot, as it interacts with them. Acceptability does not relate strictly to the user experience, but the willingness to interact with the robot is an important variable for engagement. We demonstrate the feasibility of a novel approach that, in the future, could lead to more natural human–machine interaction when delivering cognitive tests with the aid of a social robot and a Computational Psychophysiology Module (CPM). Full article

(This article belongs to the Special Issue Intelligent Human-Robot Interaction)

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23 pages, 7411 KiB

Open AccessArticle

Human Posture Transition-Time Detection Based upon Inertial Measurement Unit and Long Short-Term Memory Neural Networks

by Chun-Ting Kuo, Jun-Ji Lin, Kuo-Kuang Jen, Wei-Li Hsu, Fu-Cheng Wang, Tsu-Chin Tsao and Jia-Yush Yen

Biomimetics 2023, 8(6), 471; https://doi.org/10.3390/biomimetics8060471 - 02 Oct 2023

Cited by 3 | Viewed by 1989

Abstract

As human–robot interaction becomes more prevalent in industrial and clinical settings, detecting changes in human posture has become increasingly crucial. While recognizing human actions has been extensively studied, the transition between different postures or movements has been largely overlooked. This study explores using two deep-learning methods, the linear Feedforward Neural Network (FNN) and Long Short-Term Memory (LSTM), to detect changes in human posture among three different movements: standing, walking, and sitting. To explore the possibility of rapid posture-change detection upon human intention, the authors introduced transition stages as distinct features for the identification. During the experiment, the subject wore an inertial measurement unit (IMU) on their right leg to measure joint parameters. The measurement data were used to train the two machine learning networks, and their performances were tested. This study also examined the effect of the sampling rates on the LSTM network. The results indicate that both methods achieved high detection accuracies. Still, the LSTM model outperformed the FNN in terms of speed and accuracy, achieving 91% and 95% accuracy for data sampled at 25 Hz and 100 Hz, respectively. Additionally, the network trained for one test subject was able to detect posture changes in other subjects, demonstrating the feasibility of personalized or generalized deep learning models for detecting human intentions. The accuracies for posture transition time and identification at a sampling rate of 100 Hz were 0.17 s and 94.44%, respectively. In summary, this study achieved some good outcomes and laid a crucial foundation for the engineering application of digital twins, exoskeletons, and human intention control. Full article

(This article belongs to the Special Issue Intelligent Human-Robot Interaction)

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13 pages, 824 KiB

Open AccessArticle

Modeling and Analysis of Human Comfort in Human–Robot Collaboration

by Yuchen Yan, Haotian Su and Yunyi Jia

Biomimetics 2023, 8(6), 464; https://doi.org/10.3390/biomimetics8060464 - 01 Oct 2023

Cited by 1 | Viewed by 1172

Abstract

The emergence and recent development of collaborative robots have introduced a safer and more efficient human–robot collaboration (HRC) manufacturing environment. Since the release of COBOTs, a great amount of research efforts have been focused on improving robot working efficiency, user safety, human intention detection, etc., while one significant factor—human comfort—has been frequently ignored. The comfort factor is critical to COBOT users due to its great impact on user acceptance. In previous studies, there is a lack of a mathematical-model-based approach to quantitatively describe and predict human comfort in HRC scenarios. Also, few studies have discussed the cases when multiple comfort factors take effect simultaneously. In this study, a multi-linear-regression-based general human comfort prediction model is proposed under human–robot collaboration scenarios, which is able to accurately predict the comfort levels of humans in multi-factor situations. The proposed method in this paper tackled these two gaps at the same time and also demonstrated the effectiveness of the approach with its high prediction accuracy. The overall average accuracy among all participants is 81.33%, while the overall maximum value is 88.94%, and the overall minimum value is 72.53%. The model uses subjective comfort rating feedback from human subjects as training and testing data. Experiments have been implemented, and the final results proved the effectiveness of the proposed approach in identifying human comfort levels in HRC. Full article

(This article belongs to the Special Issue Intelligent Human-Robot Interaction)

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18 pages, 2667 KiB

Open AccessArticle

Development of a New Control System for a Rehabilitation Robot Using Electrical Impedance Tomography and Artificial Intelligence

by Alireza Abbasimoshaei, Adithya Kumar Chinnakkonda Ravi and Thorsten Alexander Kern

Biomimetics 2023, 8(5), 420; https://doi.org/10.3390/biomimetics8050420 - 11 Sep 2023

Cited by 8 | Viewed by 1322

Abstract

In this study, we present a tomography-based control system for a rehabilitation robot using a novel approach to assess advancement and a dynamic model of the system. In this model, the torque generated by the robot and the impedance of the patient’s hand are used to determine each step of the rehabilitation. In the proposed control architecture, a regression model is developed and implemented based on the extraction of tomography signals to estimate the muscles state. During the rehabilitation session, the torque applied by the patient is adjusted according to this estimation. The first step of this protocol is to calculate the subject-specific parameters. These include the axis offset, inertia parameters, passive damping and stiffness. The second step involves identifying the other elements of the model, such as the torque resulting from interaction. In this case, the robot will calculate the torque generated by the patient. The developed robot-based solution and the suggested protocol were tested on different participants and showed promising results. First, the prediction of the impedance–position relationship was evaluated, and the prediction was below 2% error. Then, different participants with different impedances were tested, and the results showed that the control system controlled the force and position for each participant individually. Full article

(This article belongs to the Special Issue Intelligent Human-Robot Interaction)

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23 pages, 1224 KiB

Open AccessArticle

Adaptive Circadian Rhythms for Autonomous and Biologically Inspired Robot Behavior

by Marcos Maroto-Gómez, María Malfaz, Álvaro Castro-González, Sara Carrasco-Martínez and Miguel Ángel Salichs

Biomimetics 2023, 8(5), 413; https://doi.org/10.3390/biomimetics8050413 - 06 Sep 2023

Viewed by 5273

Abstract

Biological rhythms are periodic internal variations of living organisms that act as adaptive responses to environmental changes. The human pacemaker is the suprachiasmatic nucleus, a brain region involved in biological functions like homeostasis or emotion. Biological rhythms are ultradian (<24 h), circadian (∼24 h), or infradian (>24 h) depending on their period. Circadian rhythms are the most studied since they regulate daily sleep, emotion, and activity. Ambient and internal stimuli, such as light or activity, influence the timing and the period of biological rhythms, making our bodies adapt to dynamic situations. Nowadays, robots experience unceasing development, assisting us in many tasks. Due to the dynamic conditions of social environments and human-robot interaction, robots exhibiting adaptive behavior have more possibilities to engage users by emulating human social skills. This paper presents a biologically inspired model based on circadian biorhythms for autonomous and adaptive robot behavior. The model uses the Dynamic Circadian Integrated Response Characteristic method to mimic human biology and control artificial biologically inspired functions influencing the robot’s decision-making. The robot’s clock adapts to light, ambient noise, and user activity, synchronizing the robot’s behavior to the ambient conditions. The results show the adaptive response of the model to time shifts and seasonal changes of different ambient stimuli while regulating simulated hormones that are key in sleep/activity timing, stress, and autonomic basal heartbeat control during the day. Full article

(This article belongs to the Special Issue Intelligent Human-Robot Interaction)

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Review

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35 pages, 5576 KiB

Open AccessReview

Deep Learning in the Ubiquitous Human–Computer Interactive 6G Era: Applications, Principles and Prospects

by Chunlei Chen, Huixiang Zhang, Jinkui Hou, Yonghui Zhang, Huihui Zhang, Jiangyan Dai, Shunpeng Pang and Chengduan Wang

Biomimetics 2023, 8(4), 343; https://doi.org/10.3390/biomimetics8040343 - 02 Aug 2023

Cited by 2 | Viewed by 1641

Abstract

With the rapid development of enabling technologies like VR and AR, we human beings are on the threshold of the ubiquitous human-centric intelligence era. 6G is believed to be an indispensable cornerstone for efficient interaction between humans and computers in this promising vision. 6G is supposed to boost many human-centric applications due to its unprecedented performance improvements compared to 5G and before. However, challenges are still to be addressed, including but not limited to the following six aspects: Terahertz and millimeter-wave communication, low latency and high reliability, energy efficiency, security, efficient edge computing and heterogeneity of services. It is a daunting job to fit traditional analytical methods into these problems due to the complex architecture and highly dynamic features of ubiquitous interactive 6G systems. Fortunately, deep learning can circumvent the interpretability issue and train tremendous neural network parameters, which build mapping relationships from neural network input (status and specific requirements of a 6G application) to neural network output (settings to satisfy the requirements). Deep learning methods can be an efficient alternative to traditional analytical methods or even conquer unresolvable predicaments of analytical methods. We review representative deep learning solutions to the aforementioned six aspects separately and focus on the principles of fitting a deep learning method into specific 6G issues. Based on this review, our main contributions are highlighted as follows. (i) We investigate the representative works in a systematic view and find out some important issues like the vital role of deep reinforcement learning in the 6G context. (ii) We point out solutions to the lack of training data in 6G communication context. (iii) We reveal the relationship between traditional analytical methods and deep learning, in terms of 6G applications. (iv) We identify some frequently used efficient techniques in deep-learning-based 6G solutions. Finally, we point out open problems and future directions. Full article

(This article belongs to the Special Issue Intelligent Human-Robot Interaction)

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22 pages, 2505 KiB

Open AccessReview

A Review of Myoelectric Control for Prosthetic Hand Manipulation

by Ziming Chen, Huasong Min, Dong Wang, Ziwei Xia, Fuchun Sun and Bin Fang

Biomimetics 2023, 8(3), 328; https://doi.org/10.3390/biomimetics8030328 - 24 Jul 2023

Cited by 7 | Viewed by 6451

Abstract

Myoelectric control for prosthetic hands is an important topic in the field of rehabilitation. Intuitive and intelligent myoelectric control can help amputees to regain upper limb function. However, current research efforts are primarily focused on developing rich myoelectric classifiers and biomimetic control methods, limiting prosthetic hand manipulation to simple grasping and releasing tasks, while rarely exploring complex daily tasks. In this article, we conduct a systematic review of recent achievements in two areas, namely, intention recognition research and control strategy research. Specifically, we focus on advanced methods for motion intention types, discrete motion classification, continuous motion estimation, unidirectional control, feedback control, and shared control. In addition, based on the above review, we analyze the challenges and opportunities for research directions of functionality-augmented prosthetic hands and user burden reduction, which can help overcome the limitations of current myoelectric control research and provide development prospects for future research. Full article

(This article belongs to the Special Issue Intelligent Human-Robot Interaction)

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