Relevant bibliographies by topics / Human intention estimation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Human intention estimation'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Human intention estimation"

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Qin, Yongming, Makoto Kumon, and Tomonari Furukawa. "Estimation of a Human-Maneuvered Target Incorporating Human Intention." Sensors 21, no. 16 (2021): 5316. http://dx.doi.org/10.3390/s21165316.

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This paper presents a new approach for estimating the motion state of a target that is maneuvered by an unknown human from observations. To improve the estimation accuracy, the proposed approach associates the recurring motion behaviors with human intentions, and models the association as an intention-pattern model. The human intentions relate to labels of continuous states; the motion patterns characterize the change of continuous states. In the preprocessing, an Interacting Multiple Model (IMM) estimation technique is used to infer the intentions and extract motions, which eventually constru
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Suzuki, Satoshi, and Fumio Harashima. "Estimation Algorithm of Machine Operational Intention by Bayes Filtering with Self-Organizing Map." Advances in Human-Computer Interaction 2012 (2012): 1–20. http://dx.doi.org/10.1155/2012/724587.

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We present an intention estimator algorithm that can deal with dynamic change of the environment in a man-machine system and will be able to be utilized for an autarkical human-assisting system. In the algorithm, state transition relation of intentions is formed using a self-organizing map (SOM) from the measured data of the operation and environmental variables with the reference intention sequence. The operational intention modes are identified by stochastic computation using a Bayesian particle filter with the trainedSOM. This method enables to omit the troublesome process to specify types
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Petković, Tomislav, David Puljiz, Ivan Marković, and Björn Hein. "Human Intention Estimation based on Hidden Markov Model Motion Validation for Safe Flexible Robotized Warehouses." Robotics and computer-integrated manufacturing 57 (December 14, 2018): 128–96. https://doi.org/10.1016/j.rcim.2018.11.004.

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With the substantial growth of logistics businesses the need for larger warehouses and their automation arises, thus using robots as assistants to human workers is becoming a priority. In order to operate efficiently and safely, robot assistants or the supervising system should recognize human intentions in real-time. Theory of mind (ToM) is an intuitive human conception of other humans' mental state, i.e., beliefs and desires, and how they cause behavior. In this paper we propose a ToM based human intention estimation algorithm for flexible robotized warehouses. We observe human's, i.
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Adeola-Bello, Zulikha Ayomikun, and Norsinnira Zainul Azlan. "Power Assist Rehabilitation Robot and Motion Intention Estimation." International Journal of Robotics and Control Systems 2, no. 2 (2022): 297–316. http://dx.doi.org/10.31763/ijrcs.v2i2.650.

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This article attempts to review papers on power assist rehabilitation robots, human motion intention, control laws, and estimation of power assist rehabilitation robots based on human motion intention in recent years. This paper presents the various ways in which human motion intention in rehabilitation can be estimated. This paper also elaborates on the control laws for the estimation of motion intention of the power assist rehabilitation robot. From the review, it has been found that the motion intention estimation method includes: Artificial Intelligence-based motion intention and Model-bas
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Alevizos, Konstantinos I., Charalampos P. Bechlioulis, and Kostas J. Kyriakopoulos. "Physical Human–Robot Cooperation Based on Robust Motion Intention Estimation." Robotica 38, no. 10 (2020): 1842–66. http://dx.doi.org/10.1017/s0263574720000958.

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SUMMARYCooperative transportation by human and robotic coworkers constitutes a challenging research field that could lead to promising technological achievements. Toward this direction, the present work demonstrates that, under a leader–follower architecture, where the human determines the object’s desired trajectory, complex cooperative object manipulation with minimal human effort may be achieved. More specifically, the robot estimates the object’s desired motion via a prescribed performance estimation law that drives the estimation error to an arbitrarily small residual set. Subsequently, t
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Liu, Zhiguang, and Jianhong Hao. "Intention Recognition in Physical Human-Robot Interaction Based on Radial Basis Function Neural Network." Journal of Robotics 2019 (April 11, 2019): 1–8. http://dx.doi.org/10.1155/2019/4141269.

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To solve synchronization movement problem in human-robot haptic collaboration, the robot is often required to recognize intention of the cooperator. In this paper, a method based on radial basis function neural network (RBFNN) model is presented to identify the motion intention of collaborator. Here, the human intention is defined as the desired velocity in human limb model, of which the estimation is obtained in real time based on interaction force and the contact point movement characteristics (current position and velocity of the robot) by the trained RBFNN model. To obtain training samples
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Li, Yanan, and Shuzhi Sam Ge. "Human–Robot Collaboration Based on Motion Intention Estimation." IEEE/ASME Transactions on Mechatronics 19, no. 3 (2014): 1007–14. http://dx.doi.org/10.1109/tmech.2013.2264533.

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Du, Guoming, Zhen Ding, Hao Guo, Meichao Song, and Feng Jiang. "Estimation of Lower Limb Joint Angles Using sEMG Signals and RGB-D Camera." Bioengineering 11, no. 10 (2024): 1026. http://dx.doi.org/10.3390/bioengineering11101026.

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Estimating human joint angles is a crucial task in motion analysis, gesture recognition, and motion intention prediction. This paper presents a novel model-based approach for generating reliable and accurate human joint angle estimation using a dual-branch network. The proposed network leverages combined features derived from encoded sEMG signals and RGB-D image data. To ensure the accuracy and reliability of the estimation algorithm, the proposed network employs a convolutional autoencoder to generate a high-level compression of sEMG features aimed at motion prediction. Considering the variab
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Yu, Xinbo, Yanan Li, Shuang Zhang, Chengqian Xue, and Yu Wang. "Estimation of human impedance and motion intention for constrained human–robot interaction." Neurocomputing 390 (May 2020): 268–79. http://dx.doi.org/10.1016/j.neucom.2019.07.104.

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Trombetta, Daniel, Ghananeel S. Rotithor, Iman Salehi, and Ashwin P. Dani. "Human Intention Estimation using Fusion of Pupil and Hand Motion." IFAC-PapersOnLine 53, no. 2 (2020): 9535–40. http://dx.doi.org/10.1016/j.ifacol.2020.12.2431.

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Dissertations / Theses on the topic "Human intention estimation"

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Durdu, Akif. "Robotic System Design For Reshaping Estimated Human Intention In Human-robot Interactions." Phd thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12615150/index.pdf.

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This thesis outlines the methodology and experiments associated with the reshaping of human intention via based on the robot movements in Human-Robot Interactions (HRI). Although works on estimating human intentions are quite well known research areas in the literature, reshaping intentions through interactions is a new significant branching in the field of human-robot interaction. In this thesis, we analyze how previously estimated human intentions change based on his/her actions by cooperating with mobile robots in a real human-robot environment. Our approach uses the Observable Operator Mod
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Duncan, Kester. "Scene-Dependent Human Intention Recognition for an Assistive Robotic System." Scholar Commons, 2014. https://scholarcommons.usf.edu/etd/5009.

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In order for assistive robots to collaborate effectively with humans for completing everyday tasks, they must be endowed with the ability to effectively perceive scenes and more importantly, recognize human intentions. As a result, we present in this dissertation a novel scene-dependent human-robot collaborative system capable of recognizing and learning human intentions based on scene objects, the actions that can be performed on them, and human interaction history. The aim of this system is to reduce the amount of human interactions necessary for communicating tasks to a robot. Accordingly,
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Yang, Chang-En, and 楊長恩. "Non-verbal Natural Interactive Human Intention Estimation Using Time-varying Fuzzy Markov Models." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/62844575681044861638.

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碩士<br>淡江大學<br>電機工程學系碩士班<br>100<br>The estimation of human intention for robot decision mechanism is the ultimate goal of this thesis. The human decision mechanism most information to exist the non-verbal language in the human communication. If the human robot interaction via the human intention of non-verbal language estimation and analysis the information then the robot decision mechanism will be similarity the human thinking and reaction. Therefore, we propose time-varying fuzzy Markov model to estimate the human intention of meaning of posture. We will via MATLAB simulation the intention st
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Book chapters on the topic "Human intention estimation"

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Luo, Jing, Chao Liu, Ning Wang, and Chenguang Yang. "A Method of Intention Estimation for Human-Robot Interaction." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29933-0_6.

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Asao, Takafumi, Satoshi Suzuki, and Kentaro Kotani. "Estimation of Driver’s Steering Intention by Using Mechanical Impedance." In Human Interface and the Management of Information. Information and Interaction Design. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39209-2_1.

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Zanina, Valeriya, Gcinizwe Dlamini, and Vadim Palyonov. "Deep Learning Based Approach for Human Intention Estimation in Lower-Back Exoskeleton." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-28073-3_12.

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Itoda, Kota, Norifumi Watanabe, and Yoshiyasu Takefuji. "Analyzing Human Decision Making Process with Intention Estimation Using Cooperative Pattern Task." In Artificial General Intelligence. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63703-7_23.

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Liu, Peter, and Chang-En Yang. "Human Intention Estimation Using Time-Varying Fuzzy Markov Models for Natural Non-verbal Human Robot Interface." In Intelligent Robotics and Applications. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33515-0_20.

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Noohi, Ehsan, and Miloš Žefran. "Estimating Human Intention During a Human–Robot Cooperative Task Based on the Internal ForceInternal force Model." In Trends in Control and Decision-Making for Human–Robot Collaboration Systems. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-40533-9_5.

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Durdu, Akif, Ismet Erkmen, Aydan M. Erkmen, and Alper Yilmaz. "Robotic Hardware and Software Integration for Changing Human Intentions." In Prototyping of Robotic Systems. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0176-5.ch013.

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Estimating and reshaping human intentions are among the most significant topics of research in the field of human-robot interaction. This chapter provides an overview of intention estimation literature on human-robot interaction, and introduces an approach on how robots can voluntarily reshape estimated intentions. The reshaping of the human intention is achieved by the robots moving in certain directions that have been a priori observed from the interactions of humans with the objects in the scene. Being among the only few studies on intention reshaping, the authors of this chapter exploit sp
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Görür, O. Can, and Aydan M. Erkmen. "Intention and Body-Mood Engineering via Proactive Robot Moves in HRI." In Handbook of Research on Synthesizing Human Emotion in Intelligent Systems and Robotics. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-7278-9.ch012.

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This chapter focuses on emotion and intention engineering by socially interacting robots that induce desired emotions/intentions in humans. The authors provide all phases that pave this road, supported by overviews of leading works in the literature. The chapter is partitioned into intention estimation, human body-mood detection through external-focused attention, path planning through mood induction and reshaping intention. Moreover, the authors present their novel concept, with implementation, of reshaping current human intention into a desired one, using contextual motions of mobile robots.
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Görür, O. Can, and Aydan M. Erkmen. "Intention and Body-Mood Engineering via Proactive Robot Moves in HRI." In Rapid Automation. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8060-7.ch012.

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This chapter focuses on emotion and intention engineering by socially interacting robots that induce desired emotions/intentions in humans. The authors provide all phases that pave this road, supported by overviews of leading works in the literature. The chapter is partitioned into intention estimation, human body-mood detection through external-focused attention, path planning through mood induction and reshaping intention. Moreover, the authors present their novel concept, with implementation, of reshaping current human intention into a desired one, using contextual motions of mobile robots.
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Gadi, Anil Lokesh. "Applying machine learning for driver assistance systems and autonomous vehicle technologies." In Deep Science Publishing. Deep Science Publishing, 2025. https://doi.org/10.70593/978-93-49307-21-6_9.

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As the number of vehicles increases worldwide, the traffic situation becomes increasingly complicated in terms of safety. The automotive industry has been developing various safety technologies, and driver assistance systems, such as headway distance control, automatic braking system and evasive steering system, have become one of the major features of a vehicle for the safety of the driver and passengers. The advanced driver assistance system (ADAS) has been developed to assist the driver for improved safety and better vehicle control. The ADAS equipped with advanced sensors and intelligent v
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Conference papers on the topic "Human intention estimation"

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Dong, Bo, Rui Sun, Tianjiao An, Chen Li, Hongbo Dong, and Bing Ma. "Adaptive Fuzzy Impedance Control of Human-Robot Interaction Modular Robot Manipulators Based on Human Motion Intention Estimation." In 2024 14th International Conference on Information Science and Technology (ICIST). IEEE, 2024. https://doi.org/10.1109/icist63249.2024.10805395.

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Wang, Yiwei, Yixuan Sheng, Ji Wang, and Wenlong Zhang. "Human Intention Estimation With Tactile Sensors in Human-Robot Collaboration." In ASME 2017 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dscc2017-5291.

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In this paper, machine learning methods are proposed for human intention estimation based on the change of force distribution on the interaction surface during human-robot collaboration (HRC). The force distribution under different human intentions are examined when the human and robot are jointly carrying the same piece of object. A pair of Robotiq tactile sensors is applied to monitor the change of force distribution on the interaction surface. Three machine learning algorithms are tested on recognition of human intentions based on the force distribution patterns on the contact surface of gr
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Zhang, Zhijie, Jianmin Zheng, and Nadia Magnenat Thalmann. "Engagement Intention Estimation in Multiparty Human-Robot Interaction." In 2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN). IEEE, 2021. http://dx.doi.org/10.1109/ro-man50785.2021.9515373.

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Shuzhi Sam Ge, Yanan Li, and Hongsheng He. "Neural-network-based human intention estimation for physical human-robot interaction." In 2011 8th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI 2011). IEEE, 2011. http://dx.doi.org/10.1109/urai.2011.6145849.

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Amirshirzad, Negin, Emre Ugur, Ozkan Bebek, and Erhan Oztop. "Adaptive Shared Control with Human Intention Estimation for Human Agent Collaboration*." In 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE). IEEE, 2022. http://dx.doi.org/10.1109/case49997.2022.9926657.

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Chen, Xiongjun, Yiming Jiang, and Chenguang Yang. "Stiffness Estimation and Intention Detection for Human-Robot Collaboration." In 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2020. http://dx.doi.org/10.1109/iciea48937.2020.9248186.

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Awais, Muhammad, and Dominik Henrich. "Proactive premature intention estimation for intuitive human-robot collaboration." In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2012). IEEE, 2012. http://dx.doi.org/10.1109/iros.2012.6385880.

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Cheng, Yalin, Pengfei Yi, Rui Liu, Jing Dong, Dongsheng Zhou, and Qiang Zhang. "Human-robot Interaction Method Combining Human Pose Estimation and Motion Intention Recognition." In 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 2021. http://dx.doi.org/10.1109/cscwd49262.2021.9437772.

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Yokoyama, Ayami, and Takashi Omori. "Modeling of human intention estimation process in social interaction scene." In 2010 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2010. http://dx.doi.org/10.1109/fuzzy.2010.5584042.

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Chen, Fei, Baiqing Sun, Jian Huang, Hironobu Sasaki, and Toshio Fukuda. "Human intention estimation algorithm design for robot in human and robot cooperated cell assembly." In 2010 International Symposium on Micro-NanoMechatronics and Human Science (MHS). IEEE, 2010. http://dx.doi.org/10.1109/mhs.2010.5669497.

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