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LIM, HEONYOUNG, YEONSIK KANG, JOONGJAE LEE, JONGWON KIM, and BUM-JAE YOU. "SOFTWARE ARCHITECTURE AND TASK DEFINITION OF A MULTIPLE HUMANOID COOPERATIVE CONTROL SYSTEM." International Journal of Humanoid Robotics 06, no. 02 (2009): 173–203. http://dx.doi.org/10.1142/s0219843609001747.
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This paper presents a cooperative control software architecture that coordinates a team of multiple humanoid to complete a mission by collaborating with each other. The mission of the humanoid team is decomposed into tasks and distributed to each humanoid to be executed. Each task is described by the proposed humanoid action primitives, which are designed to abstract broad classes of humanoid tasks appropriately. In particular, missions and tasks for the humanoid team are designed by using a finite state machine with a developed user interface. The multiple humanoid cooperative control software consists of 3 layers: the mission layer, task layer, and action layer. The software architecture has scalability to the number of humanoids and the number of assigned missions with its framework based on the CORBA middleware, which integrates many different functionalities of the humanoid. The feasibility and robustness of the implemented software architecture are verified through successful completion of the mission assigned to the humanoid team while each humanoid performs its given task sequentially.
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PETRILLI-BARCELÓ, ALBERTO, HERIBERTO CASARRUBIAS-VARGAS, MIGUEL BERNAL-MARIN, EDUARDO BAYRO-CORROCHANO, and RÜDIGER DILLMAN. "GEOMETRIC TECHNIQUES FOR HUMANOID PERCEPTION." International Journal of Humanoid Robotics 07, no. 03 (2010): 429–50. http://dx.doi.org/10.1142/s0219843610002234.
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In this article, we propose a conformal model for 3D visual perception. In our model, the two views are fused in an extended 3D horopter model. For visual simultaneous localization and mapping (SLAM), an extended Kalman filter (EKF) technique is used for 3D reconstruction and determination of the robot head pose. In addition, the Viola and Jones machine-learning technique is applied to improve the robot relocalization. The 3D horopter, the EKF-based SLAM, and the Viola and Jones machine-learning technique are key elements for building a strong real-time perception system for robot humanoids. A variety of interesting experiments show the efficiency of our system for humanoid robot vision.
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S., Hamidreza Mohades Kasaei, Mohammadreza Mohades Kasaei S., Alireza Mohades Kasaei S., A. Monadjemi S., and Taheri Mohsen. "BRAIN Journal - Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller." BRAIN - Broad Research in Artificial Intelligence and Neuroscience 1, no. 3 (2010): 57–74. https://doi.org/10.5281/zenodo.1036446.
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ABSTRACT Research on humanoid robotics in Mechatronics and Automation Laboratory, Electrical and Computer Engineering, Islamic Azad University Khorasgan branch (Isfahan) of Iran was started at the beginning of this decade. Various research prototypes for humanoid robots have been designed and are going through evolution over these years. This paper describes the hardware and software design of the kid size humanoid robot systems of the PERSIA Team in 2009. The robot has 20 actuated degrees of freedom based on Hitec HSR898. In this paper we have tried to focus on areas such as mechanical structure, Image processing unit, robot controller, Robot AI and behavior learning. In 2009, our developments for the Kid size humanoid robot include: (1) the design and construction of our new humanoid robots (2) the design and construction of a new hardware and software controller to be used in our robots. The project is described in two main parts: Hardware and Software. The software is developed a robot application which consists walking controller, autonomous motion robot, self localization base on vision and Particle Filter, local AI, Trajectory Planning, Motion Controller and Network. The hardware consists of the mechanical structure and the driver circuit board. Each robot is able to walk, fast walk, pass, kick and dribble when it catches the ball. These humanoids have been successfully participating in various robotic soccer competitions. This project is still in progress and some new interesting methods are described in the current report
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Muhammad Ali Asghar, Anum Aslam, Salheen Bakhet, et al. "An Efficient Integration of Artificial Intelligence-based Mobile Robots in Critical Frames for the Internet of Medical Things (IoMTs) Using (ADP2S) and Convolutional Neural Networks (CNNs)." Annual Methodological Archive Research Review 3, no. 4 (2025): 160–83. https://doi.org/10.63075/m3vc4e28.
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Moving in complex environments is an essential capability of intelligent mobile robots. Decades of research and engineering have been dedicated to developing sophisticated navigation systems to move mobile robots from one point to another. Despite their overall success, a recently emerging research thrust is devoted to developing machine learning techniques to address the same problem, based in large part on the success of deep learning techniques. Real-time systems are widely used in industry, including technological process control systems, industrial automation systems, SCADA systems, testing, and measuring equipment, and robotics. Artificial intelligence-based Mobile robots have been receiving attention from researchers worldwide in recent years, especially in developing autonomous mobile robots. Artificial intelligence and Machine learning play a great role in the development of humanoid robots, they have increased humanoids efficiency and their functionality. This paper presents an optimal machine learning-assisted intelligent Convolutional Neural Network (CNN) based approach for humanoid function identification using AI and Machine learning that enable humanoid robots to evolve Human-Robot Interaction (HRI) that helps resolve crucial issues concurrently while discussing improvements in Accuracy, Precision, decision-making, and interaction skills. The paper also tests and trains the ML model using the open source dataset named direct kinematics of an IRB 120 robotic. The proposed P-CNN outperformed the other renowned algorithm designs by evaluating the performance by considering the real-time sensor data, machine learning models, and natural language processing. The proposed technique demonstrates the practical uses of humanoid robotics technologies, highlighting notable accomplishments in areas like better locomotion and human-robot interaction. Despite the encouraging progress we achieved, safety and efficiently learning the representation of non-expert strategies on large-scale real-world data using reinforcement learning remain challenging. The implementation results proved that this system operated effectively with a minimal response delay of 0.77–2.67s and a high detection accuracy (98.25%) in two experimental cases, which makes it suitable for real-time applications. This article also addresses the prospective opportunities for further research and development in humanoid robotics while suggesting further advancements in this field that could result from interdisciplinary efforts.. Key words: Mobile robot navigation, Machine learning, Motion planning, Motion control, ResNet, Deep neural network, CNN, Healthcare, Prediction models, Segmentation
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Zebrowski, Robin L. "Fear of a Bot Planet: Anthropomorphism, Humanoid Embodiment, and Machine Consciousness." Journal of Artificial Intelligence and Consciousness 07, no. 01 (2020): 119–32. http://dx.doi.org/10.1142/s2705078520500071.
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There is an ongoing debate about the existence of humanoid robotics. The arguments tend to focus on the ethical claims that there is always deception involved. However, little attention has been paid to the ontological reasons that humanoid robotics are valuable in consciousness research. This paper examines the arguments and controversy around ethical rejection of humanoid robotics, while also summarizing some of the landscape of 4e cognition that highlights the ways our specific humanoid bodies in our specific cultural, social, and physical environments play an indispensable role in cognition, from conceptualization through communication. Ultimately, we argue that there is a compelling set of reasons to pursue humanoid robotics as a major research agenda in AI if the goal is to create an artificial conscious system that we will be able to both recognize as conscious and communicate with successfully.
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Oh, Jun Ho. "Humanoid Robot: A Machine That Walks." IFAC Proceedings Volumes 39, no. 16 (2006): 2–5. http://dx.doi.org/10.3182/20060912-3-de-2911.00003.
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:, Mohammed Viquaruddin. "Humanoid Laws and Society." Humanoid Laws and Society 2, no. 1 (2013): 01–03. https://doi.org/10.5281/zenodo.3404526.
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: Material, Machine, Money these three Ms sail the society on banks of a culture in recent which is depends on mechanical life of human form of mechanics/techniques were different in earlier era however phases of human developmental evolution leads its life to material on contrary history thru mythology proves their differences with less material. Great philosophers, leaders like Hobbes, M Gandhi and others constructed their views on it. Metropolis and big cities tainted it like machine life which is well defined as humanoid. This monograph is an attempt to re-search large vicinity thru a small pound that with obvious recent social outlook, regional regain, and right of the same said society with obvious patriotic integrity to revalue the human society.
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Gruchoła, Małgorzata. "Wartość ciała biologicznego i ciała humanoida w świetle założeń teologii ciała." Kultura Słowian Rocznik Komisji Kultury Słowian PAU 19 (2023): 215–27. http://dx.doi.org/10.4467/25439561ksr.23.015.18992.
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The Value of the Biological Body and the Humanoid Body in the Context of the Theology of the Body The aim of this paper was to present the influence of changes in the system: biological body – mechanical body (humanoid body), resulting from the use of cyborgization and social robotics on the evaluation of the biological body, in the context of the assumptions of the theology of the body (based on the biological body). The article adopts three research perspectives encompassing the perception of: 1. the biological body; 2. the body of a cyborg, i.e. a hybrid of a human and a machine (a machine in a biological body, inside the body, and a biological body functioning in a machine); 3. the mechanical body (humanoid), in the context of the theology of the body.
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Nordmann, Alfred. "Humanoid and machine artificial intelligence in science fictions." Semiotic studies 2, no. 4 (2022): 15–21. http://dx.doi.org/10.18287/2782-2966-2022-2-4-15-21.
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There are a lot of different debates about artificial intelligence. Indeed, in the last several decades one can witness a shift in Hollywood SciFi movies. Many of the older stories were puzzled about human and machine identities as they appeared to become uncannily indistinguishable. More recent stories deal with disappointment and shock as the machines which emulate and exceed human reasoning prove to have a kind of intelligence that is very different from human thinking and feeling. The following observations seek to reflect the significance of this shift. On the one hand, it is referred to a change in the orientation of AI research itself - transition from humanoid AI to machine one. On the other hand, these movies do not so much analyze impact and future of two AI kinds, as they reflect on the cultural conditions, hopes and fears that motivate or ground AI research. Thus, science fiction can be a stage or a representational framework for an interpretative interaction with technology, that is, for understanding artificial intelligence, its significance and importance for various ways people understand and position themselves in the world of signs and symbols, objects and work products.
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Jiménez Moreno, Robinson, Oscar Aviles, and Ruben Darío Hernández Beleño. "Humanoid Robot Cooperative System by Machine Vision." International Journal of Online Engineering (iJOE) 13, no. 12 (2017): 162. http://dx.doi.org/10.3991/ijoe.v13i12.7594.
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This article presents a supervised control position system, based on image processing and oriented to the cooperative work between two humanoid robots that work autonomously. The first robot picks up an object, carry it to the second robot and after that the same second robot places it in an endpoint, this is achieved through doing movements in straight line trajectories and turns of 180 degrees. Using for this the Microsoft Kinect , finding for each robot and the reference object its exact spatial position, through the color space conversion and filtering, derived from the information of the RGB camera that counts and obtains this result using the information transmitted from the depth sensor, obtaining the final location of each. Through programming in C #, and the developed algorithms that allow to command each robot in order to work together for transport the reference object, from an initial point, delivering this object from one robot to the other and depositing it in an endpoint. This experiment was tested performed the same trajectory, under uniform light conditions, achieving each time the successful delivering of the object
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K, Karthika, Nityashree ., AryaRa ., and Tabassum B Shivanagi. "Humanoid Robotics in Artificial Intelligence." International Research Journal of Computer Science 10, no. 05 (2023): 250–56. http://dx.doi.org/10.26562/irjcs.2023.v1005.26.
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The humanoid robotics is the emerging and the fascinating field of research as it combines the advancements in artificial intelligence, machine learning and robotics to design socio- intelligent and human-like robots and robots resembling the human body in the structure .Art of humanoid robotics which include integration of artificial intelligent techniques to enhance their cognitive abilities, interactive behavior and perceptual capabilities like perception and sensing, natural language processing ,human robot interaction, autonomy and control, personalization and adaptability, collaborative capabilities. Overall artificial intelligence plays a crucial role in enhancing the capabilities of humanoid robotics. Machine learning is the one of the ways of implementing the social intelligence in humanoid robots. Humanoid robot is defined as robot resembling the human body and can interact with external world and exhibit basic locomotion like gestures and response to real time scenarios. Domestic robots like poppy developed and designed by poppy project and NAO developed by Softbank. Humanoid robots are used in healthcare, public-relations, education ,rescue operations, research ,human -robot interactions, personal assistance, and education sectors . Robots have proved to be good human companion by doing basic human chores and by robot human interactions. The presented research focuses on the development of 2D,3D indoor humanoid robots in a cost-effective manner which are capable of doing basic human household chores, understanding the human emotions, care giving, personal assistance and in education sector as social robot as an assistive tool in classrooms by accepting the voice commands and responding through the hand gestures and by biped or wheeled locomotion .
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Nuralem, Abizov, Yuan Huang Jia, and Gao Fei. "Developing a Humanoid Robot Platform." International Journal of Engineering and Management Research 8, no. 3 (2018): 66–70. https://doi.org/10.31033/ijemr.8.3.9.
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This paper is focused on developing a platform that helps researchers to create verify and implement their machine learning algorithms to a humanoid robot in real environment. The presented platform is durable, easy to fix, upgrade, fast to assemble and cheap. Also, using this platform we present an approach that solves a humanoid balancing problem, which uses only fully connected neural network as a basic idea for real time balancing. The method consists of 3 main conditions: 1) using different types of sensors detect the current position of the body and generate the input information for the neural network, 2) using fully connected neural network produce the correct output, 3) using servomotors make movements that will change the current position to the new one. During field test the humanoid robot can balance on the moving platform that tilts up to 10 degrees to any direction. Finally, we have shown that using our platform we can do research and compare different neural networks in similar conditions which can be important for the researchers to do analyses in machine learning and robotics.
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ARSENIO, ARTUR M. "DEVELOPMENT OF NEURAL MECHANISMS FOR MACHINE LEARNING." International Journal of Neural Systems 15, no. 01n02 (2005): 41–54. http://dx.doi.org/10.1142/s0129065705000050.
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The goal of this work is to develop a humanoid robot's perceptual mechanisms through the use of learning aids. We describe methods to enable learning on a humanoid robot using learning aids such as books, drawing materials, boards, educational videos or other children toys. Visual properties of objects are learned and inserted into a recognition scheme, which is then applied to acquire new object representations — we propose learning through developmental stages. Inspired in infant development, we will also boost the robot's perceptual capabilities by having a human caregiver performing educational and play activities with the robot (such as drawing, painting or playing with a toy train on a railway). We describe original algorithms to extract meaningful percepts from such learning experiments. Experimental evaluation of the algorithms corroborates the theoretical framework.
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Kalita, Jeet. "Humanoid: Max." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 4020–24. http://dx.doi.org/10.22214/ijraset.2022.44844.
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Abstract: Nowadays there are many assistants like Google voice assistant , Alexa…etc. Humanoid assistant is a assistant which move freely with you while assisting in your daily task such as informing you about today’s weather. We have deeply studied some of the best research papers on Robotic, Machine learning and some very necessities of AutoML, so in order to get to a conclusion of what works the best for us. We have also studied about many other research papers on voice assistant as to how to create a basic voice assistant. The main idea behind this project is to make a bot which can move freely that can assist you in day-to-day tasks. It will respond and talk to you like your own friend. It will also work as your personal house assistant. In this project we have basically worked on robotics. The aim of our project is to make a virtual Voice Assistant bot. The main motive is to first design the Humanoid and then program the features using our knowledge of programming. The working is based on Arduino microcontroller and Raspberry Pi microprocessor. The code is simulated on software (IDE) and later we interfaced with the hardware. We picked this as our design as robotics has come a long way and has become a part of our everyday life and also has a wide compass in the engineering field. For us the main task was to make such a model such a model in an similar a way, where the tasks are performed in an automated way so as to save time and costs.
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Zhu, Zhen Chao, Zhen Sui, Yan Tao Tian, and Hong Jiang. "Modeling and Control of Passive Dynamic Walking Robot with Humanoid Gait." Applied Mechanics and Materials 461 (November 2013): 903–7. http://dx.doi.org/10.4028/www.scientific.net/amm.461.903.
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Considering the sagittal movement and the lateral swing in the humanoid practical walking, a new humanoid passive dynamic bipedal robot with the lateral movable upper body is proposed in this paper. The finite state machine (FSM) theory is adopted to control the robot, which changes agilely the control strategy according to the practical states of the humanoid gait. In the method, the torque compensation adaptive excitation control strategy is used for sagittal control and PID is applied to the upper body for the robots lateral stability. It is verified by the co-simulation based on ADAMS and MATLAB that the bipedal robot can reach the stable humanoid gait with the high energy efficiency.
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Yen, Hsueh-Chuan, Tai-Chang Hsia, and Ren-Chieh Liao. "Machine Learning in a Humanoid Intelligent Service Robot." Journal of Information and Optimization Sciences 35, no. 2 (2014): 129–41. http://dx.doi.org/10.1080/02522667.2013.867737.
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Li, Ning, Tie Yang, Yang Yang, et al. "Bioinspired Musculoskeletal Model-based Soft Wrist Exoskeleton for Stroke Rehabilitation." Journal of Bionic Engineering 17, no. 6 (2020): 1163–74. http://dx.doi.org/10.1007/s42235-020-0101-9.
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AbstractExoskeleton robots have demonstrated the potential to rehabilitate stroke dyskinesia. Unfortunately, poor human-machine physiological coupling causes unexpected damage to human of muscles and joints. Moreover, inferior humanoid kinematics control would restrict human natural kinematics. Failing to deal with these problems results in bottlenecks and hinders its application. In this paper, the simplified muscle model and muscle-liked kinematics model were proposed, based on which a soft wrist exoskeleton was established to realize natural human interaction. Firstly, we simplified the redundant muscular system related to the wrist joint from ten muscles to four, so as to realize the human-robot physiological coupling. Then, according to the above human-like musculoskeletal model, the humanoid distributed kinematics control was established to achieve the two DOFs coupling kinematics of the wrist. The results show that the wearer of an exoskeleton could reduce muscle activation and joint force by 43.3% and 35.6%, respectively. Additionally, the humanoid motion trajectories similarity of the robot reached 91.5%. Stroke patients could recover 90.3% of natural motion ability to satisfy for most daily activities. This work provides a fundamental understanding on human-machine physiological coupling and humanoid kinematics control of the exoskeleton robots for reducing the post-stroke complications.
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Stephens, John, and Mio Bryce. "‘Nothing dirty about turning on a machine’: Loving your Mechanoid in Contemporary Manga." Papers: Explorations into Children's Literature 14, no. 2 (2004): 44–54. http://dx.doi.org/10.21153/pecl2004vol14no2art1267.
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Relationships between humans and humanoid machines, like robots, androids and physical embodiments of computer programs, render permeable the boundary between human and machine, nature and culture, born and made. Artificial intelligence entities are shown with a capacity for emotional development and on the other hand people become cyborged under social and familial pressures to perform the roles expected of them and basic communication is through the mediation of technology.
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Dehnert, Marco. "Sex With Robots and Human-Machine Sexualities: Encounters Between Human-Machine Communication and Sexuality Studies." Human-Machine Communication 4 (2022): 131–50. http://dx.doi.org/10.30658/hmc.4.7.
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Sex robots are a controversial topic. Understood as artificial-intelligence enhanced humanoid robots designed for use in partnered and solo sex, sex robots offer ample opportunities for theorizing from a Human-Machine Communication (HMC) perspective. This comparative literature review conjoins the seemingly disconnected literatures of HMC and sexuality studies (SeS) to explore questions surrounding intimacy, love, desire, sex, and sexuality among humans and machines. In particular, I argue for understanding human-machine sexualities as communicative sexuotechnical-assemblages, extending previous efforts in both HMC and SeS for more-than-human, ecological, and more fluid approaches to humans and machines, as well as to sex and sexuality. This essay continues and expands the critical turn in HMC by engaging in an interdisciplinary exercise with theoretical, design, and use/effect implications in the context of sex robots.
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Kalyanakrishnan, Shivaram, and Ambarish Goswami. "Predicting Falls of a Humanoid Robot through Machine Learning." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 2 (2010): 1793–98. http://dx.doi.org/10.1609/aaai.v24i2.18815.
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Although falls are undesirable in humanoid robots, they are also inevitable, especially as robots get deployed in physically interactive human environments. We consider the problem of fall prediction, i.e., to predict if a robot's balance controller can prevent a fall from the current state. A trigger from the fall predictor is used to switch the robot from a balance maintenance mode to a fall control mode. Hence, it is desirable for the fall predictor to signal imminent falls with sufficient lead time before the actual fall, while minimizing false alarms. Analytical techniques and intuitive rules fail to satisfy these competing objectives on a large robot that is subjected to strong disturbances and therefore exhibits complex dynamics. Today effective supervised learning tools are available for finding patterns in high-dimensional data. Our paper contributes a novel approach to engineer fall data such that a supervised learning method can be exploited to achieve reliable prediction. Specifically, we introduce parameters to control the tradeoff between the false positive rate and lead time. Several parameter combinations yield solutions that improve both the false positive rate and the lead time of hand-coded solutions. Learned predictors are decision lists with typical depths of 5-10, in a 16-dimensional feature space. Experiments are carried out in simulation on an Asimo-like robot.
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Ivancevic, V. G., and M. Snoswell. "Fuzzy-stochastic functor machine for general humanoid-robot dynamics." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 31, no. 3 (2001): 319–30. http://dx.doi.org/10.1109/3477.931514.
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Ivancevic, Vladimir, and Nicholas Beagley. "Brain-like functor control machine for general humanoid biodynamics." International Journal of Mathematics and Mathematical Sciences 2005, no. 11 (2005): 1759–79. http://dx.doi.org/10.1155/ijmms.2005.1759.
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A novel, brain-like, hierarchical (affine-neuro-fuzzy-topological) control for biomechanically realistic humanoid-robot biodynamics (HB), formulated previously in [15, 16], is proposed in the form of a tensor-invariant, “meta-cybernetic” functor machine. It represents a physiologically inspired, three-level, nonlinear feedback controller of muscular-like joint actuators. On the spinal level, nominal joint-trajectory tracking is formulated as an affine Hamiltonian control system, resembling the spinal (autogenetic-reflex) “motor servo.” On the cerebellar level, a feedback-control map is proposed in the form of self-organized, oscillatory, neurodynamical system, resembling the associative interaction of excitatory granule cells and inhibitory Purkinje cells. On the cortical level, a topological “hyper-joystick” command space is formulated with a fuzzy-logic feedback-control map defined on it, resembling the regulation of locomotor conditioned reflexes. Finally, both the cerebellar and the cortical control systems are extended to provide translational force control for moving6-degree-of-freedom chains of inverse kinematics.
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Zhang, Yu, Ruochen Wang, Yuelin Zhou, Ying Ding, Shanshan Ye, and Ruochen Wang. "Bibliometric analysis and visualization of research and development trends in humanoid robotics." Advances in Engineering Technology Research 10, no. 1 (2024): 648. http://dx.doi.org/10.56028/aetr.10.1.648.2024.
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This study searched the Web of Science core database for literature related to robotic arms for humanoid robots and used bibliometrics and CiteSpace 6.1.R2 software to visually analyze the authors, institutions, and keywords of the literature published in this field from 2009 to 2023. The study summarizes the current status and trends of robotic arms for humanoid robots and incorporates information extraction to select and include 376 articles for analysis. This study provides the field's development vein by reviewing the relevant literature and follows the research progress in the area of humanoid robots in a particular vein. In addition to thoroughly examining robotic arms and motion generating techniques for robotic arms that mimic human action, this study reviews pertinent literature. A review of the literature indicates a clear trend toward the use of artificial intelligence and machine learning to create motion control schemes for humanoid robotic arms. The report then assesses current methodologies and suggests future research directions for promising studies.
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Ge, Qirui. "Applications of Reinforcement Learning on Humanoid Robot Controlling." Highlights in Science, Engineering and Technology 124 (February 18, 2025): 108–14. https://doi.org/10.54097/qm1n5316.
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Amidst the swift advancement of artificial intelligence and machine learning technologies, humanoid robots are increasingly demonstrating their potential in mimicking human motions, adapting to intricate environments, and executing a wide array of tasks. Reinforcement Learning (RL), as an advanced learning paradigm that optimizes decision-making processes through environmental interaction, has emerged as a pivotal tool in enhancing the capabilities of humanoid robots. In this paper, the researcher delves into a variety of RL techniques and their implementations, spotlighting key accomplishments and addressing the prevailing challenges alongside envisaging future trajectories. Through an in-depth examination of these applications, our aim is to elucidate the transformative potential of RL in the domain of humanoid robotics, paving the way for more adaptive, intelligent, and autonomous systems. This paper posits that, with the deepening of research and technological progress, RL will catalyze breakthroughs in the humanoid robot sector, propelling smart robots towards greater integration within human society.
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Pawan P, Mr. "PATIENT MONITORING HUMANOID ROBOT FOR PANDEMIC SITUATIONS." International Scientific Journal of Engineering and Management 03, no. 04 (2024): 1–9. http://dx.doi.org/10.55041/isjem01524.
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Humanoid robots are a rapidly evolving field of research and development that aims to create robots with human- l ike characteristics and capabilities. These robots possess a wide range of applications, from assisting humans in various tasks to exploring environments that are hazardous or inaccessible to humans. The development of humanoid robot involves the integration of advanced technologies, including robotics, artificial intelligence, computer vision, and natural language processing. This abstract explores the key aspects of humanoid robots, including their design, locomotion, perception, cognition, and interaction capabilities. It discusses the challenges involved in creating humanoid robots that can navigate complex environments, recognize and manipulate objects, understand and respond to human speech and gestures, and exhibit social behaviours. The abstract also highlights the potential benefits and ethical considerations associated with humanoid robots, such as their role in healthcare, education, e n t e r t a i n m e n t , a n d d i s a s t e r r e s p o n s e . Furthermore, the abstract addresses ongoing research efforts and technological advancements in the field of humanoid robotics, including the development of more dexterous and agile robot improvements in human- robot interaction, and the integration of machine learning and deep learning techniques. It emphasizes the importance of interdisciplinary collaboration and the need for robust hardware and software solutions to overcome the challenges faced in creating humanoid robots. Keywords—ATmega8 microcontroller, DHT Sensors, Heart Beat Sensor,LCD.
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Chamola, Vinay, Ankur Vineet, Anand Nayyar, and Eklas Hossain. "Brain-Computer Interface-Based Humanoid Control: A Review." Sensors 20, no. 13 (2020): 3620. http://dx.doi.org/10.3390/s20133620.
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A Brain-Computer Interface (BCI) acts as a communication mechanism using brain signals to control external devices. The generation of such signals is sometimes independent of the nervous system, such as in Passive BCI. This is majorly beneficial for those who have severe motor disabilities. Traditional BCI systems have been dependent only on brain signals recorded using Electroencephalography (EEG) and have used a rule-based translation algorithm to generate control commands. However, the recent use of multi-sensor data fusion and machine learning-based translation algorithms has improved the accuracy of such systems. This paper discusses various BCI applications such as tele-presence, grasping of objects, navigation, etc. that use multi-sensor fusion and machine learning to control a humanoid robot to perform a desired task. The paper also includes a review of the methods and system design used in the discussed applications.
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Mishra, Deepti, Karen Parish, Ricardo Gregorio Lugo, and Hao Wang. "A Framework for Using Humanoid Robots in the School Learning Environment." Electronics 10, no. 6 (2021): 756. http://dx.doi.org/10.3390/electronics10060756.
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With predictions of robotics and efficient machine learning being the building blocks of the Fourth Industrial Revolution, countries need to adopt a long-term strategy to deal with potential challenges of automation and education must be at the center of this long-term strategy. Education must provide students with a grounding in certain skills, such as computational thinking and an understanding of robotics, which are likely to be required in many future roles. Targeting an acknowledged gap in existing humanoid robot research in the school learning environment, we present a multidisciplinary framework that integrates the following four perspectives: technological, pedagogical, efficacy of humanoid robots and a consideration of the ethical implications of using humanoid robots. Further, this paper presents a proposed application, evaluation and a case study of how the framework can be used.
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Suzuki, Kenji, Riku Hikiji, and Shuji Hashimoto. "Development of an Autonomous Humanoid Robot, iSHA, for Harmonized Human-Machine Environment." Journal of Robotics and Mechatronics 14, no. 5 (2002): 497–505. http://dx.doi.org/10.20965/jrm.2002.p0497.
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Our research aim is to build a harmonized humanmachine environment where people and machines can interact with each other naturally, seamlessly, and intuitively. Based on the sketched scenario, we have developed a humanoid robot, iSHA (interactive Systems for Humanoid Agent), designed to behave like and interact with people. We implemented an intelligent robotic architecture that integrates goal-oriented subsystems by taking into consideration the flexibility and scalability of the system. iSHA has an upper body resembling a human in shape and a mobile base with two wheels. The upper body with a head and two arms has 24 DOF. Two wheels equipped under the body provide safe and robust locomotion. Each eye equipped with a small CCD camera, small microphones built into the head, and touch sensory devices on the body respectively provide the robot with binocular vision, auditory and touch sensing ability.
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Kajita, Shuuji. "A Humanoid Robot is Just a Machine, Do You Know?" Journal of the Robotics Society of Japan 31, no. 9 (2013): 830–32. http://dx.doi.org/10.7210/jrsj.31.830.
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Hirose, Shigeo. "Intelligent Robotics. From Humanoid to Robot with Emerged Machine Intelligence." Journal of the Robotics Society of Japan 16, no. 5 (1998): 607–11. http://dx.doi.org/10.7210/jrsj.16.607.
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Yang, Liang, Qingtao Han, and Chunjian Deng. "Walking control of humanoid robot based on extreme learning machine." International Journal of Automation and Control 10, no. 4 (2016): 375. http://dx.doi.org/10.1504/ijaac.2016.079537.
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S Bhoopalan, Ragunath P, Sanjay K, and Subash M. "Adaptive Autonomous Assistance Using Rasberry Pi." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 03 (2025): 734–39. https://doi.org/10.47392/irjaeh.2025.0102.
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This project focuses on the development of a general-purpose humanoid robot designed to perform a wide range of tasks across diverse environments. The robot aims to enhance human-robot collaboration by integrating advanced features such as natural language processing, gesture recognition, and facial expression analysis to facilitate seamless and intuitive interaction. Machine learning algorithms are embedded to enable the robot to adapt and improve its functionality over time by learning from user interactions and experiences. Additionally, the design emphasizes energy efficiency, reliability, and cost-effectiveness to make the system scalable for potential mass production. The project envisions a socially aware humanoid robot capable of operating in settings ranging from home care to office assistance, contributing to automation and human productivity. By addressing limitations in existing humanoid systems, the project aspires to create an intelligent and adaptable solution for real-world applications.
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Hdid, Jalal, Oussama Lamsellak, Ahmad Benlghazi, Abdelhamid Benali, and Ouafae El Melhaoui. "Embedded systems and artificial intelligence for enhanced humanoid robotics applications." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 2 (2025): 1912. https://doi.org/10.11591/ijece.v15i2.pp1912-1923.
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This paper presents a method for collecting precise hand gesture (HG) data using a low-cost embedded device for an embedded artificial intelligence (EAI)-based humanoid robotics (HR) application. Despite advancements in the field, challenges remain in deploying cost-effective methods for accurately capturing and recognizing body gesture data. The ultimate objective is to develop humanoid robots (HRS) capable of better understanding human activities and providing optimal daily life support. In this regard, our approach utilizes a Raspberry Pi Pico microcontroller with a 3-axis accelerometer and a 3-axis gyroscope motion sensor to capture real- time HG data, describing ten distinct real-world tasks performed by the hand in experimental scenarios. Collected data is stored on a personal computer (PC) via a micro-python program, forming a dataset where tasks are classified using ten supervised machine learning (SML) models. Two classification experiments were conducted: the first involved predicting raw data, and the second applied normalization and feature extraction (FE) techniques to improve prediction performance. The results showed promising accuracy in the first phase (89% max), with further improvements achieved in the second phase (94% max). Finally, by employing similar methods, we can integrate highly trained machine learning (ML) models into embedded humanoid robotic systems, enabling real-time human assistance.
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Hdid, Jalal, Oussama Lamsellak, Ahmad Benlghazi, Abdelhamid Benali, and Ouafae El Melhaoui. "Embedded systems and artificial intelligence for enhanced humanoid robotics applications." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 2 (2025): 1912–23. https://doi.org/10.11591/ijece.v15i2.pp1912-1923.
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This paper presents a method for collecting precise hand gesture (HG) data using a low-cost embedded device for an embedded artificial intelligence (EAI)-based humanoid robotics (HR) application. Despite advancements in the field, challenges remain in deploying cost-effective methods for accurately capturing and recognizing body gesture data. The ultimate objective is to develop humanoid robots (HRS) capable of better understanding human activities and providing optimal daily life support. In this regard, our approach utilizes a Raspberry Pi Pico microcontroller with a 3-axis accelerometer and a 3-axis gyroscope motion sensor to capture real- time HG data, describing ten distinct real-world tasks performed by the hand in experimental scenarios. Collected data is stored on a personal computer (PC) via a micro-python program, forming a dataset where tasks are classified using ten supervised machine learning (SML) models. Two classification experiments were conducted: the first involved predicting raw data, and the second applied normalization and feature extraction (FE) techniques to improve prediction performance. The results showed promising accuracy in the first phase (89% max), with further improvements achieved in the second phase (94% max). Finally, by employing similar methods, we can integrate highly trained machine learning (ML) models into embedded humanoid robotic systems, enabling real-time human assistance.
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Berns, Karsten, and Zuhair Zafar. "Emotion based human-robot interaction." MATEC Web of Conferences 161 (2018): 01001. http://dx.doi.org/10.1051/matecconf/201816101001.
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Human-machine interaction is a major challenge in the development of complex humanoid robots. In addition to verbal communication the use of non-verbal cues such as hand, arm and body gestures or mimics can improve the understanding of the intention of the robot. On the other hand, by perceiving such mechanisms of a human in a typical interaction scenario the humanoid robot can adapt its interaction skills in a better way. In this work, the perception system of two social robots, ROMAN and ROBIN of the RRLAB of the TU Kaiserslautern, is presented in the range of human-robot interaction.
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Suroor, Naaima, Imran Hussain, Aqeel Khalique, and Tabrej Ahamad Khan. "Analyzing the Effects of Reinforcement Learning to Develop Humanoid Robots." International Journal of End-User Computing and Development 8, no. 1 (2019): 55–66. http://dx.doi.org/10.4018/ijeucd.20190101.oa2.
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Reinforcement learning is a flourishing machine learning concept that has greatly influenced how robots are designed and taught to solve problems without human intervention. Robotics is not an alien discipline anymore, and we have several great innovations in this field that promise to impact lives for the better. However, humanoid robots are still a baffling concept for scientists, although we have managed to develop a few great inventions which look, talk, work, and behave very similarly to humans. But, can these machines actually exhibit the cognitive abilities of judgment, problem-solving, and perception as well as humans? In this article, the authors analyzed the probable impact and aspects of robots and their potential to behave like humans in every possible way through reinforcement learning techniques. The paper also discusses the gap between 'natural' and 'artificial' knowledge.
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Zhu, Zhichao, Xiangjuan Bai, Zening Lin, Yuze Xu, Shanjun Chen, and Zirong Luo. "Kinematic Analysis of a Centrally Driven Humanoid Robotic Arm." Journal of Physics: Conference Series 2483, no. 1 (2023): 012061. http://dx.doi.org/10.1088/1742-6596/2483/1/012061.
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Abstract The humanoid robotic arm has a broad prospect in the field of human-machine cooperation, but the large inertia of the current joint robotic arm slows down its response speed and running speed. In this study, a centrally driven humanoid robotic arm is introduced, which has the characteristics of small inertia, good rapidity, and high load. Screw Theory is used to establish the kinematic model and analyze that the robotic arm has no singular configuration in the working range. The geometric method and Monte Carlo method are used to analyze and verify its workspace, which is the basis for the subsequent dynamic analysis and control of the robotic arm.
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Akanksha, Hon, Shelke Riya, Hatekar Sharvari, Shelke Mayuri, and B. Gawali M. "Identification of Human Disease (Diabetic Retinopathy) using Convolutional Neural Network." Journal of Image Processing and Artificial Intelligence 6, no. 2 (2020): 1–5. https://doi.org/10.5281/zenodo.3813329.
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As the world is changing in a rapid way, the technology has taken places in human’s day to day life. Humans are also willing to include artificial machines (technology) which can work efficiently with less failure. But it is a challenge for human to build an artificial humanoid as same as human. Identification and classification are the basic steps by which machine can work same as human works. The identification and classification of input images propose a big challenge to make machine more accurate. To classify the input with higher rate of an accuracy is the challenge for the researchers, scientists. To overcome this issue, we have proposed a modified Convolutional Neural Network (CNN) method to recognize images correctly to reduce failure.
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Athira, KH, U. Aswathi, Jaleel Deena, and P. Dijesh. "The Role of Humanoid Robots in Modern Society: A Technological Perspective." Research and Reviews: Advancement in Robotics 8, no. 2 (2025): 8–19. https://doi.org/10.5281/zenodo.14998249.
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<em>Humanoid robots are designed to replicate human-like abilities and integrate AI, cognitive architectures, and IoT for advanced autonomy. They exhibit sensory perception, decision-making, adaptability, and human-robot interaction, enabling them to function efficiently in real-world applications. Machine learning and deep learning enhance their intelligence, allowing them to learn from experiences and optimize performance.</em> <em>IoT plays a crucial role by enabling connectivity, automation, and remote monitoring, using Wi-Fi, Zigbee, RFID, and GSM modules for real-time communication. These advancements have expanded humanoid robot applications in healthcare, education, security, and personal assistance. Additionally, multi-robot systems enhance disaster response, using ground reconnaissance robots, aerial drones, and AI-driven navigation for search and rescue missions.</em> <em>Despite progress, challenges like common-sense reasoning, real-time adaptation, and ethical concerns remain. Future developments focus on improving AGI, human-like learning, and ethical AI frameworks, making humanoid robots more intelligent and socially integrated.</em>
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Connan, Mathilde, Marek Sierotowicz, Bernd Henze, et al. "Learning to teleoperate an upper-limb assistive humanoid robot for bimanual daily-living tasks." Biomedical Physics & Engineering Express 8, no. 1 (2021): 015022. http://dx.doi.org/10.1088/2057-1976/ac3881.
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Abstract Objective. Bimanual humanoid platforms for home assistance are nowadays available, both as academic prototypes and commercially. Although they are usually thought of as daily helpers for non-disabled users, their ability to move around, together with their dexterity, makes them ideal assistive devices for upper-limb disabled persons, too. Indeed, teleoperating a bimanual robotic platform via muscle activation could revolutionize the way stroke survivors, amputees and patients with spinal injuries solve their daily home chores. Moreover, with respect to direct prosthetic control, teleoperation has the advantage of freeing the user from the burden of the prosthesis itself, overpassing several limitations regarding size, weight, or integration, and thus enables a much higher level of functionality. Approach. In this study, nine participants, two of whom suffer from severe upper-limb disabilities, teleoperated a humanoid assistive platform, performing complex bimanual tasks requiring high precision and bilateral arm/hand coordination, simulating home/office chores. A wearable body posture tracker was used for position control of the robotic torso and arms, while interactive machine learning applied to electromyography of the forearms helped the robot to build an increasingly accurate model of the participant’s intent over time. Main results. All participants, irrespective of their disability, were uniformly able to perform the demanded tasks. Completion times, subjective evaluation scores, as well as energy- and time- efficiency show improvement over time on short and long term. Significance. This is the first time a hybrid setup, involving myoeletric and inertial measurements, is used by disabled people to teleoperate a bimanual humanoid robot. The proposed setup, taking advantage of interactive machine learning, is simple, non-invasive, and offers a new assistive solution for disabled people in their home environment. Additionnally, it has the potential of being used in several other applications in which fine humanoid robot control is required.
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An, Xiaojun. "The typification of infringement issues of humanoid robots: with special reference to the governance of infringement caused by learning algorithms." Advances in Social Behavior Research 16, no. 5 (2025): None. https://doi.org/10.54254/2753-7102/2025.24455.
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Humanoid robots possess technical features such as external human-like appearance, intelligence, and human-machine hybrid control. These may lead to the anthropomorphic trap, expanding the risk of infringement and complicating the attribution of liability. Different causes of infringement result in different types of infringement, which have different focuses in legal practice. Therefore, on the basis of clear classification standards, a typified discussion can be conducted. After dividing them into two major types: passive infringement and active infringement, further subdivisions can be made to clarify the nature and resolution of each type of infringement. Among the various types of infringement, the type caused by learning algorithms is the most distinctive due to its autonomous occurrence and difficulty in explanation. The method of law and economics can be utilized to allocate responsibilities among the relevant parties involved in the humanoid robot industry chain: humanoid robot manufacturers should follow the dynamic national regulations based on their development stage and different application scenarios under the guidance of the Hand Formula; users should be responsible for their negligent behavior due to failure to fulfill reasonable care obligations; and providers of general artificial intelligence models may be held jointly liable with humanoid robot product providers if they fail to fulfill the responsibility of transparency.
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Kulk, Jason, and James S. Welsh. "Perturbation Sensing for Humanoid Robots using a Multiclass Support Vector Machine." IFAC Proceedings Volumes 43, no. 18 (2010): 709–16. http://dx.doi.org/10.3182/20100913-3-us-2015.00053.
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Böhlen, Marc. "Robots with Bad Accents: Living with Synthetic Speech." Leonardo 41, no. 3 (2008): 209–14. http://dx.doi.org/10.1162/leon.2008.41.3.209.
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Synthetic speech technologies have a profound impact on how we think about and interact with computers. This text discusses parts I and II of the “Make Language Project,” a trilogy on the cultural fallout of machine generated speech as a conduit for reconsidering prejudices in synthetic speech production and humanoid robot design.
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DILLMANN, RÜDIGER, REGINE BECHER, and PETER STEINHAUS. "ARMAR II — A LEARNING AND COOPERATIVE MULTIMODAL HUMANOID ROBOT SYSTEM." International Journal of Humanoid Robotics 01, no. 01 (2004): 143–55. http://dx.doi.org/10.1142/s0219843604000046.
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This paper gives an overview on current and forthcoming research activities of the Collaborative Research Center 588 "Humanoid Robots — Learning and Cooperating Multimodal Robots" which is located in Karlsruhe, Germany. Its research activities can be divided into the following areas: mechatronic robot system components like lightweight 7 DOF arms, 5-fingered dexterous hands, an active sensor head and a spine type central body and skills of the humanoid robot system; multimodal man-machine interfaces; augmented reality for modeling and simulation of robots, environment and user; and finally, cognitive abilities. Some of the research activities are described in this paper, and we introduce the application scenario testing the robot system. In particular, we present a robot teaching center and the execution which is of type "household."
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Rizgi, Aulia Khilmi, Anhar Risnumawan, Fernando Ardila, et al. "Visual Perception System of EROS Humanoid Robot Soccer." International Journal of Intelligent Information Technologies 16, no. 4 (2020): 68–86. http://dx.doi.org/10.4018/ijiit.2020100105.
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In this paper, a humanoid robot soccer perception system, consisting of a ball, field detection, and localization, is developed in order to deal with the new rules in RoboCup. Color segmentation and image morphology are improved together with a more sophisticated machine learning algorithm to detect a soccer ball robustly. Those algorithms are still favorable due to its real-time running in most of the embedded platform. For localization, the field is divided into pre-deðned grids and employing k-NN (k-nearest neighbor) to determine the robot location in the grids. Pre-defined grids are used to reduce computation due to matching with a map. Experiment results show that the developed system relatively well for adapting to the new rules update.
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Zhou, Zijun, Shuqin Yang, Zhisen Ni, Weixing Qian, Cuihong Gu, and Zekun Cao. "Pedestrian Navigation Method Based on Machine Learning and Gait Feature Assistance." Sensors 20, no. 5 (2020): 1530. http://dx.doi.org/10.3390/s20051530.
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In recent years, as the mechanical structure of humanoid robots increasingly resembles the human form, research on pedestrian navigation technology has become of great significance for the development of humanoid robot navigation systems. To solve the problem that the wearable inertial navigation system based on micro-inertial measurement units (MIMUs) installed on feet cannot effectively realize its positioning function when the body movement is too drastic to be measured correctly by commercial grade inertial sensors, a pedestrian navigation method based on construction of a virtual inertial measurement unit (VIMU) and gait feature assistance is proposed. The inertial data from different positions of pedestrians’ lower limbs are collected synchronously via actual IMUs as training samples. The nonlinear mapping relationship between inertial information from the human foot and leg is established by a visual geometry group-long short term memory (VGG-LSTM) neural network model, based on which the foot VIMU and virtual inertial navigation system (VINS) are constructed. The VINS experimental results show that, combined with zero-velocity update (ZUPT), the integrated method of error modification proposed in this paper can effectively reduce the accumulation of positioning errors in situations where the gait type exceeds the measurement range of the inertial sensors. The positioning performance of the proposed method is more accurate and stable in complex gait types than that merely using ZUPT.
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Ramos, Joao, and Sangbae Kim. "Dynamic locomotion synchronization of bipedal robot and human operator via bilateral feedback teleoperation." Science Robotics 4, no. 35 (2019): eaav4282. http://dx.doi.org/10.1126/scirobotics.aav4282.
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Despite remarkable progress in artificial intelligence, autonomous humanoid robots are still far from matching human-level manipulation and locomotion proficiency in real applications. Proficient robots would be ideal first responders to dangerous scenarios such as natural or man-made disasters. When handling these situations, robots must be capable of navigating highly unstructured terrain and dexterously interacting with objects designed for human workers. To create humanoid machines with human-level motor skills, in this work, we use whole-body teleoperation to leverage human control intelligence to command the locomotion of a bipedal robot. The challenge of this strategy lies in properly mapping human body motion to the machine while simultaneously informing the operator how closely the robot is reproducing the movement. Therefore, we propose a solution for this bilateral feedback policy to control a bipedal robot to take steps, jump, and walk in synchrony with a human operator. Such dynamic synchronization was achieved by (i) scaling the core components of human locomotion data to robot proportions in real time and (ii) applying feedback forces to the operator that are proportional to the relative velocity between human and robot. Human motion was sped up to match a faster robot, or drag was generated to synchronize the operator with a slower robot. Here, we focused on the frontal plane dynamics and stabilized the robot in the sagittal plane using an external gantry. These results represent a fundamental solution to seamlessly combine human innate motor control proficiency with the physical endurance and strength of humanoid robots.
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Baothman, Fatmah Abdulrahman. "A Machine Learning Approach for Improving the Movement of Humanoid NAO’s Gaits." Wireless Communications and Mobile Computing 2021 (September 22, 2021): 1–14. http://dx.doi.org/10.1155/2021/1496364.
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A humanoid robot’s development requires an incredible combination of interdisciplinary work from engineering to mathematics, software, and machine learning. NAO is a humanoid bipedal robot designed to participate in football competitions against humans by 2050, and speed is crucial for football sports. Therefore, the focus of the paper is on improving NAO speed. This paper is aimed at testing the hypothesis of whether the humanoid NAO walking speed can be improved without changing its physical configuration. The applied research method compares three classification techniques: artificial neural network (ANN), Naïve Bayes, and decision tree to measure and predict NAO’s best walking speed, then select the best method, and enhance it to find the optimal average velocity speed. According to Aldebaran documentation, the real NAO’s robot default walking speed is 9.52 cm/s. The proposed work was initiated by studying NAO hardware platform limitations and selecting Nao’s gait 12 parameters to measure the accuracy metrics implemented in the three classification models design. Five experiments were designed to model and trace the changes for the 12 parameters. The preliminary NAO’s walking datasets open-source available at GitHub, the NAL, and RoboCup datasheets are implemented. All generated gaits’ parameters for both legs and feet in the experiments were recorded using the Choregraphe software. This dataset was divided into 30% for training and 70% for testing each model. The recorded gaits’ parameters were then fed to the three classification models to measure and predict NAO’s walking best speed. After 500 training cycles for the Naïve Bayes, the decision tree, and ANN, the RapidMiner scored 48.20%, 49.87%, and 55.12%, walking metric speed rate, respectively. Next, the emphasis was on enhancing the ANN model to reach the optimal average velocity walking speed for the real NAO. With 12 attributes, the maximum accuracy metric rate of 65.31% was reached with only four hidden layers in 500 training cycles with a 0.5 learning rate for the best walking learning process, and the ANN model predicted the optimal average velocity speed of 51.08% without stiffness: V 1 = 22.62 cm / s , V 2 = 40 cm / s , and V = 30 cm / s . Thus, the tested hypothesis holds with the ANN model scoring the highest accuracy rate for predicting NAO’s robot walking state speed by taking both legs to gauge joint 12 parameter values.
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Popescu, Aura-Loredana, Nirvana Popescu, Ciprian Dobre, Elena-Simona Apostol, and Decebal Popescu. "IoT and AI-Based Application for Automatic Interpretation of the Affective State of Children Diagnosed with Autism." Sensors 22, no. 7 (2022): 2528. http://dx.doi.org/10.3390/s22072528.
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In the context in which it was demonstrated that humanoid robots are efficient in helping children diagnosed with autism in exploring their affective state, this paper underlines and proves the efficiency of a previously developed machine learning-based mobile application called PandaSays, which was improved and integrated with an Alpha 1 Pro robot, and discusses performance evaluations using deep convolutional neural networks and residual neural networks. The model trained with MobileNet convolutional neural network had an accuracy of 56.25%, performing better than ResNet50 and VGG16. A strategy for commanding the Alpha 1 Pro robot without its native application was also established and a robot module was developed that includes the communication protocols with the application PandaSays. The output of the machine learning algorithm involved in PandaSays is sent to the humanoid robot to execute some actions as singing, dancing, and so on. Alpha 1 Pro has its own programming language—Blockly—and, in order to give the robot specific commands, Bluetooth programming is used, with the help of a Raspberry Pi. Therefore, the robot motions can be controlled based on the corresponding protocols. The tests have proved the robustness of the whole solution.
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Straßmann, Carolin, Nicole C. Krämer, Hendrik Buschmeier, and Stefan Kopp. "Age-Related Differences in the Evaluation of a Virtual Health Agent’s Appearance and Embodiment in a Health-Related Interaction: Experimental Lab Study." Journal of Medical Internet Research 22, no. 4 (2020): e13726. http://dx.doi.org/10.2196/13726.
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Background Assistive technologies have become more important owing to the aging population, especially when they foster healthy behaviors. Because of their natural interface, virtual agents are promising assistants for people in need of support. To engage people during an interaction with these technologies, such assistants need to match the users´ needs and preferences, especially with regard to social outcomes. Objective Prior research has already determined the importance of an agent’s appearance in a human-agent interaction. As seniors can particularly benefit from the use of virtual agents to maintain their autonomy, it is important to investigate their special needs. However, there are almost no studies focusing on age-related differences with regard to appearance effects. Methods A 2×4 between-subjects design was used to investigate the age-related differences of appearance effects in a human-agent interaction. In this study, 46 seniors and 84 students interacted in a health scenario with a virtual agent, whose appearance varied (cartoon-stylized humanoid agent, cartoon-stylized machine-like agent, more realistic humanoid agent, and nonembodied agent [voice only]). After the interaction, participants reported on the evaluation of the agent, usage intention, perceived presence of the agent, bonding toward the agent, and overall evaluation of the interaction. Results The findings suggested that seniors evaluated the agent more positively (liked the agent more and evaluated it as more realistic, attractive, and sociable) and showed more bonding toward the agent regardless of the appearance than did students. In addition, interaction effects were found. Seniors reported the highest usage intention for the cartoon-stylized humanoid agent, whereas students reported the lowest usage intention for this agent. The same pattern was found for participant bonding with the agent. Seniors showed more bonding when interacting with the cartoon-stylized humanoid agent or voice only agent, whereas students showed the least bonding when interacting with the cartoon-stylized humanoid agent. Conclusions In health-related interactions, target group–related differences exist with regard to a virtual assistant’s appearance. When elderly individuals are the target group, a humanoid virtual assistant might trigger specific social responses and be evaluated more positively at least in short-term interactions.