{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T12:21:28Z","timestamp":1784031688812,"version":"3.55.0"},"reference-count":122,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,1,4]],"date-time":"2025-01-04T00:00:00Z","timestamp":1735948800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"iREHAB: AI-powered Robotic Personalized Rehabilitation","award":["ISCIII-AES-2022\/003041"],"award-info":[{"award-number":["ISCIII-AES-2022\/003041"]}]},{"name":"iREHAB: AI-powered Robotic Personalized Rehabilitation","award":["Y2020\/NMT-666"],"award-info":[{"award-number":["Y2020\/NMT-666"]}]},{"name":"iREHAB: AI-powered Robotic Personalized Rehabilitation","award":["PLEC2021-007819"],"award-info":[{"award-number":["PLEC2021-007819"]}]},{"name":"ISCIII","award":["ISCIII-AES-2022\/003041"],"award-info":[{"award-number":["ISCIII-AES-2022\/003041"]}]},{"name":"ISCIII","award":["Y2020\/NMT-666"],"award-info":[{"award-number":["Y2020\/NMT-666"]}]},{"name":"ISCIII","award":["PLEC2021-007819"],"award-info":[{"award-number":["PLEC2021-007819"]}]},{"name":"Proyectos Sin\u00e9rgicos de I+D la Comunidad de Madrid","award":["ISCIII-AES-2022\/003041"],"award-info":[{"award-number":["ISCIII-AES-2022\/003041"]}]},{"name":"Proyectos Sin\u00e9rgicos de I+D la Comunidad de Madrid","award":["Y2020\/NMT-666"],"award-info":[{"award-number":["Y2020\/NMT-666"]}]},{"name":"Proyectos Sin\u00e9rgicos de I+D la Comunidad de Madrid","award":["PLEC2021-007819"],"award-info":[{"award-number":["PLEC2021-007819"]}]},{"name":"EU structural funds","award":["ISCIII-AES-2022\/003041"],"award-info":[{"award-number":["ISCIII-AES-2022\/003041"]}]},{"name":"EU structural funds","award":["Y2020\/NMT-666"],"award-info":[{"award-number":["Y2020\/NMT-666"]}]},{"name":"EU structural funds","award":["PLEC2021-007819"],"award-info":[{"award-number":["PLEC2021-007819"]}]},{"name":"MCIN\/AEI\/10.13039\/501100011033","award":["ISCIII-AES-2022\/003041"],"award-info":[{"award-number":["ISCIII-AES-2022\/003041"]}]},{"name":"MCIN\/AEI\/10.13039\/501100011033","award":["Y2020\/NMT-666"],"award-info":[{"award-number":["Y2020\/NMT-666"]}]},{"name":"MCIN\/AEI\/10.13039\/501100011033","award":["PLEC2021-007819"],"award-info":[{"award-number":["PLEC2021-007819"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Robotic manipulators are highly valuable tools that have become widespread in the industry, as they can achieve great precision and velocity in pick and place as well as processing tasks. However, to unlock their complete potential, some problems such as inverse kinematics (IK) need to be solved: given a Cartesian target, a method is needed to find the right configuration for the robot to reach that point. Another issue that needs to be addressed when dealing with robotic manipulators is the obstacle avoidance problem. Workspaces are usually cluttered and the manipulator should be able to avoid colliding with objects that could damage it, as well as with itself. Two alternatives exist to do this: a controller can be designed that computes the best action for each moment given the manipulator\u2019s state, or a sequence of movements can be planned to be executed by the robot. Classical approaches to all these problems, such as numeric or analytical methods, can produce precise results but take a high computation time and do not always converge. Learning-based methods have gained considerable attention in tackling the IK problem, as well as motion planning and control. These methods can reduce the computational cost and provide results for every situation avoiding singularities. This article presents a literature review of the advances made in the past five years in the use of Deep Neural Networks (DNN) for IK with regard to control and planning with and without obstacles for rigid robotic manipulators. The literature has been organized in several categories depending on the type of DNN used to solve the problem. The main contributions of each reference are reviewed and the best results are presented in summary tables.<\/jats:p>","DOI":"10.3390\/a18010023","type":"journal-article","created":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T06:43:04Z","timestamp":1736145784000},"page":"23","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["A Review on Inverse Kinematics, Control and Planning for Robotic Manipulators With and Without Obstacles via Deep Neural Networks"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-5891-2446","authenticated-orcid":false,"given":"Ana","family":"Calzada-Garcia","sequence":"first","affiliation":[{"name":"RoboticsLab, Systems and Automation Engineering Department, University Carlos III of Madrid, 28911 Legan\u00e9s, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3080-3467","authenticated-orcid":false,"given":"Juan G.","family":"Victores","sequence":"additional","affiliation":[{"name":"RoboticsLab, Systems and Automation Engineering Department, University Carlos III of Madrid, 28911 Legan\u00e9s, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3145-1342","authenticated-orcid":false,"given":"Francisco J.","family":"Naranjo-Campos","sequence":"additional","affiliation":[{"name":"RoboticsLab, Systems and Automation Engineering Department, University Carlos III of Madrid, 28911 Legan\u00e9s, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4864-4625","authenticated-orcid":false,"given":"Carlos","family":"Balaguer","sequence":"additional","affiliation":[{"name":"RoboticsLab, Systems and Automation Engineering Department, University Carlos III of Madrid, 28911 Legan\u00e9s, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,4]]},"reference":[{"key":"ref_1","unstructured":"Paul, R.P. (1981). Robot Manipulators: Mathematics, Programming, and Control: The Computer Control of Robot Manipulators, MIT Press."},{"key":"ref_2","unstructured":"Murray, R.M., Li, Z., and Sastry, S.S. (1994). A mathematical Introduction to Robotic Manipulation, CRC Press."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Latombe, J.C. (1991). Robot Motion Planning, Springer Science & Business Media.","DOI":"10.1007\/978-1-4615-4022-9"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Lewis, F.L., Dawson, D.M., and Abdallah, C.T. (2003). Robot Manipulator Control: Theory and Practice, CRC Press.","DOI":"10.1201\/9780203026953"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"117","DOI":"10.21278\/TOF.481055523","article-title":"An Analytical Inverse Kinematics Solution with the Avoidance of Joint Limits, Singularity and the Simulation of 7-DOF Anthropomorphic Manipulators","volume":"48","author":"Chou","year":"2024","journal-title":"Trans. FAMENA"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zheng, L., Lin, P., Liang, M., Wang, C., Li, Y., Sun, J., Han, Y., and Liu, H. (2024). Analytical Inverse Kinematics for a Prismatic-Revolute Hybrid Joints Radiography Robot Mounted on the Ambulance. IEEE\/ASME Trans. Mechatronics, 1\u201311.","DOI":"10.1109\/TMECH.2024.3442782"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"102970","DOI":"10.1016\/j.mechatronics.2023.102970","article-title":"Machine learning-based framework for optimally solving the analytical inverse kinematics for redundant manipulators","volume":"91","author":"Vu","year":"2023","journal-title":"Mechatronics"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1111\/cgf.13310","article-title":"Inverse kinematics techniques in computer graphics: A survey","volume":"37","author":"Aristidou","year":"2018","journal-title":"Comput. Graph. Forum"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4903","DOI":"10.1109\/TCYB.2024.3372989","article-title":"Simple inverse kinematics computation considering joint motion efficiency","volume":"54","author":"Yonezawa","year":"2024","journal-title":"IEEE Trans. Cybern."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"87909","DOI":"10.1109\/ACCESS.2024.3418206","article-title":"Variable step sizes for iterative Jacobian-based inverse kinematics of robotic manipulators","volume":"12","author":"Colan","year":"2024","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"498","DOI":"10.18178\/ijmerr.10.9.498-504","article-title":"Apply some meta-heuristic algorithms to solve inverse kinematic problems of a 7-DoFs manipulator robot","volume":"10","author":"Nguyen","year":"2021","journal-title":"Int. J. Mech. Eng. Robot. Res."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Li, M., and Qiao, L. (2023). A Review and Comparative Study of Differential Evolution Algorithms in Solving Inverse Kinematics of Mobile Manipulator. Symmetry, 15.","DOI":"10.3390\/sym15051080"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1923","DOI":"10.1109\/LRA.2024.3349927","article-title":"Modifications of Fully Resampled PSO in the Inverse Kinematics of Robot Manipulators","volume":"9","author":"Santos","year":"2024","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Li, Q., Cang, N., Zhang, W., Guo, D., and Zhang, C. (2023, January 27\u201329). A Pseudo-Inverse Redundancy-Based Resolution Scheme at the Acceleration Level to Control Robotic Arm Motion. Proceedings of the 2023 6th International Conference on Robotics, Control and Automation Engineering (RCAE), Suzhou, China.","DOI":"10.1109\/RCAE59706.2023.10398754"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1109\/56.2083","article-title":"A fast procedure for computing the distance between complex objects in three-dimensional space","volume":"4","author":"Gilbert","year":"1988","journal-title":"IEEE J. Robot. Autom."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1177\/027836498600500106","article-title":"Real-time obstacle avoidance for manipulators and mobile robots","volume":"5","author":"Khatib","year":"1986","journal-title":"Int. J. Robot. Res."},{"key":"ref_17","unstructured":"Lee, K.K., and Buss, M. (November, January 29). Obstacle avoidance for redundant robots using Jacobian transpose method. Proceedings of the 2007 IEEE\/RSJ International Conference on Intelligent Robots and Systems, San Diego, CA, USA."},{"key":"ref_18","first-page":"278","article-title":"Real-time strategy for obstacle avoidance in redundant manipulators","volume":"Volume 91","author":"Scoccia","year":"2021","journal-title":"Advances in Italian Mechanism Science. IFToMM ITALY 2020. Mechanisms and Machine Science"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wei, K., and Ren, B. (2018). A method on dynamic path planning for robotic manipulator autonomous obstacle avoidance based on an improved RRT algorithm. Sensors, 18.","DOI":"10.3390\/s18020571"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"102196","DOI":"10.1016\/j.rcim.2021.102196","article-title":"Path planning for manipulators based on an improved probabilistic roadmap method","volume":"72","author":"Chen","year":"2021","journal-title":"Robot. Comput.-Integr. Manuf."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liu, R., Nageotte, F., Zanne, P., de Mathelin, M., and Dresp-Langley, B. (2021). Deep reinforcement learning for the control of robotic manipulation: A focussed mini-review. Robotics, 10.","DOI":"10.3390\/robotics10010022"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"64","DOI":"10.5772\/58562","article-title":"Performance evaluation of the various training algorithms and network topologies in a neural-network-based inverse kinematics solution for robots","volume":"11","author":"Sari","year":"2014","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1007\/s41315-023-00274-2","article-title":"A review of recent trend in motion planning of industrial robots","volume":"7","author":"Tamizi","year":"2023","journal-title":"Int. J. Intell. Robot. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Noroozi, F., Daneshmand, M., and Fiorini, P. (2023). Conventional, Heuristic and Learning-Based Robot Motion Planning: Reviewing Frameworks of Current Practical Significance. Machines, 11.","DOI":"10.3390\/machines11070722"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"51840","DOI":"10.1109\/ACCESS.2024.3385426","article-title":"Advancements in Deep Reinforcement Learning and Inverse Reinforcement Learning for Robotic Manipulation: Towards Trustworthy, Interpretable, and Explainable Artificial Intelligence","volume":"12","author":"Ozalp","year":"2024","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Toche Tchio, G.M., Kenfack, J., Kassegne, D., Menga, F.D., and Ouro-Djobo, S.S. (2024). A comprehensive review of supervised learning algorithms for the diagnosis of photovoltaic systems, Proposing a new approach using an ensemble learning algorithm. Appl. Sci., 14.","DOI":"10.3390\/app14052072"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"109727","DOI":"10.1016\/j.dib.2023.109727","article-title":"ARKOMA dataset: An open-source dataset to develop neural networks-based inverse kinematics model for NAO robot arms","volume":"51","author":"Nugroho","year":"2023","journal-title":"Data Brief"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Rathnam, R., and Godfrey, W.W. (2023, January 15\u201317). Data Driven Approach for Inverse Kinematics in 2D and 3D. Proceedings of the 2023 IEEE 7th Conference on Information and Communication Technology (CICT), Jabalpur, India.","DOI":"10.1109\/CICT59886.2023.10455509"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"109","DOI":"10.54097\/vejx7557","article-title":"Inverse Kinematics Implementation Techniques in Robotics","volume":"81","author":"Zhang","year":"2024","journal-title":"Highlights Sci. Eng. Technol."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Bouzid, R., Narayan, J., and Gritli, H. (2024). Solving Inverse Kinematics Problem for Manipulator Robots Using Artificial Neural Network with Varied Dataset Formats. Complex Systems and Their Applications, Springer Nature.","DOI":"10.1007\/978-3-031-51224-7_4"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"24","DOI":"10.2478\/acss-2024-0004","article-title":"ANN approach for SCARA robot inverse kinematics solutions with diverse datasets and optimisers","volume":"29","author":"Bouzid","year":"2024","journal-title":"Appl. Comput. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"126327","DOI":"10.1016\/j.neucom.2023.126327","article-title":"Deep neural networks in the cloud: Review, applications, challenges and research directions","volume":"545","author":"Chan","year":"2023","journal-title":"Neurocomputing"},{"key":"ref_33","first-page":"85","article-title":"Performance analysis of data-driven techniques for solving inverse kinematics problems","volume":"Volume 294","author":"Semwal","year":"2022","journal-title":"Intelligent Systems and Applications. IntelliSys 2021"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Semwal, V.B., Reddy, M., and Narad, A. (July, January 30). Comparative study of inverse kinematics using data driven and fabrik approach. Proceedings of the 2021 5th International Conference on Advances in Robotics, Kanpur India.","DOI":"10.1145\/3478586.3478620"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"330","DOI":"10.31763\/ijrcs.v3i2.1017","article-title":"Forward and Inverse Kinematics Solution of A 3-DOF Articulated Robotic Manipulator Using Artificial Neural Network","volume":"3","author":"Sharkawy","year":"2023","journal-title":"Int. J. Robot. Control Syst."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Bouzid, R., Narayan, J., and Gritli, H. (2024, January 18\u201320). Investigating neural network hyperparameter variations in robotic arm inverse kinematics for different arm lengths. Proceedings of the 2024 Third International Conference on Power, Control and Computing Technologies (ICPC2T), Raipur, India.","DOI":"10.1109\/ICPC2T60072.2024.10474947"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1729881420925283","DOI":"10.1177\/1729881420925283","article-title":"Utilization of multilayer perceptron for determining the inverse kinematics of an industrial robotic manipulator","volume":"18","author":"Mrzljak","year":"2021","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"106682","DOI":"10.1016\/j.cie.2020.106682","article-title":"An analytical and a Deep Learning model for solving the inverse kinematic problem of an industrial parallel robot","volume":"151","author":"Toquica","year":"2021","journal-title":"Comput. Ind. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Lu, J., Zou, T., and Jiang, X. (2022). A neural network based approach to inverse kinematics problem for general six-axis robots. Sensors, 22.","DOI":"10.3390\/s22228909"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"107175","DOI":"10.1016\/j.engappai.2023.107175","article-title":"Artificial Neural Networks for inverse kinematics problem in articulated robots","volume":"126","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_41","first-page":"24","article-title":"MLP neural network for a kinematic control of a redundant planar manipulator","volume":"Volume 85","year":"2022","journal-title":"Advances in Mechanism Design III. TMM 2020. Mechanisms and Machine Science"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Hlav\u00e1\u010d, V. (2022, January 16\u201318). Inverted Kinematics of a Redundant Manipulator with a MLP Neural Network. Proceedings of the 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Male, Maldives.","DOI":"10.1109\/ICECCME55909.2022.9987898"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1007\/978-3-031-44207-0_38","article-title":"Cycleik: Neuro-inspired inverse kinematics","volume":"Volume 14254","author":"Habekost","year":"2023","journal-title":"Artificial Neural Networks and Machine Learning\u2014ICANN 2023"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1604","DOI":"10.26599\/TST.2024.9010011","article-title":"Dynamic Modeling of Robotic Manipulator via an Augmented Deep Lagrangian Network","volume":"29","author":"Wu","year":"2024","journal-title":"Tsinghua Sci. Technol."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Li, Z., Wu, S., Chen, W., and Sun, F. (2024). Extrapolation of Physics-Inspired Deep Networks in Learning Robot Inverse Dynamics. Mathematics, 12.","DOI":"10.3390\/math12162527"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"6999","DOI":"10.1109\/TNNLS.2021.3084827","article-title":"A survey of convolutional neural networks: Analysis, applications, and prospects","volume":"33","author":"Li","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Wen, X., Wang, Y., Zhu, Q., Wu, J., Xiong, R., and Xie, A. (2023). Design of recognition algorithm for multiclass digital display instrument based on convolution neural network. Biomim. Intell. Robot., 3.","DOI":"10.1016\/j.birob.2023.100118"},{"key":"ref_48","first-page":"343","article-title":"Solving inverse kinematics of a 7-DOF manipulator using convolutional neural network","volume":"Volume 1153","author":"Elkholy","year":"2020","journal-title":"Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020)"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Kumhar, H.S., and Kukshal, V. (2022). Inverse kinematic solution for 6-r industrial robot manipulator using convolution neural network. Recent Trends in Product Design and Intelligent Manufacturing Systems, Springer. Lecture Notes in Mechanical Engineering.","DOI":"10.1007\/978-981-19-4606-6_84"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"106301","DOI":"10.1016\/j.engappai.2023.106301","article-title":"Analytical and deep learning approaches for solving the inverse kinematic problem of a high degrees of freedom robotic arm","volume":"123","author":"Wagaa","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"110179","DOI":"10.1016\/j.cie.2024.110179","article-title":"Deep learning-based predicting and compensating method for the pose deviations of parallel robots","volume":"191","author":"Zhu","year":"2024","journal-title":"Comput. Ind. Eng."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201322). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Mienye, I.D., Swart, T.G., and Obaido, G. (2024). Recurrent neural networks: A comprehensive review of architectures, variants, and applications. Information, 15.","DOI":"10.20944\/preprints202408.0748.v1"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Shaar, A., and Ghaeb, J.A. (2023, January 22\u201324). Intelligent Solution for Inverse Kinematic of Industrial Robotic Manipulator Based on RNN. Proceedings of the 2023 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), Amman, Jordan.","DOI":"10.1109\/JEEIT58638.2023.10185778"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Jiang, G., Luo, M., Bai, K., and Chen, S. (2017). A precise positioning method for a puncture robot based on a PSO-optimized BP neural network algorithm. Appl. Sci., 7.","DOI":"10.3390\/app7100969"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Wang, S., Zhang, Y., Chen, S., Xu, M., Yu, Y., and Liu, P. (2023, January 16\u201318). Inverse Kinematics Analysis of 5-DOF Cooperative Robot Based on Long Short-Term Memory Network. Proceedings of the 2023 IEEE 3rd International Conference on Software Engineering and Artificial Intelligence (SEAI), Xiamen, China.","DOI":"10.1109\/SEAI59139.2023.10217721"},{"key":"ref_57","unstructured":"Tsai, M.T., King, C.T., and Ho, C.K. (2021, January 11\u201313). Exploiting Joint Dependencies for Data-driven Inverse Kinematics with Neural Networks for High-DOF Robot Arms. Proceedings of the ISCA 34th International Conference on Computer Applications in Industry and Engineering, EPiC Series in Computing, Online."},{"key":"ref_58","unstructured":"Kong, L. (2020). Kinematic resolutions of redundant robot manipulators using integration-enhanced RNNs. arXiv."},{"key":"ref_59","unstructured":"Bensadoun, R., Gur, S., Blau, N., and Wolf, L. (2022, January 17\u201323). Neural inverse kinematic. Proceedings of the International Conference on Machine Learning, PMLR, Baltimore, MD, USA."},{"key":"ref_60","unstructured":"Vaishnavi, J., Singh, B., Vijayvargiya, A., and Kumar, R. (2022, January 14\u201317). Deep Learning Framework for Inverse Kinematics Mapping for a 5 DoF Robotic Manipulator. Proceedings of the 2022 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), Jaipur, India."},{"key":"ref_61","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative adversarial nets. Proceedings of the Advances in Neural Information Processing Systems 27, Montreal, QC, Canada."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"103386","DOI":"10.1016\/j.robot.2019.103386","article-title":"Learning inverse kinematics and dynamics of a robotic manipulator using generative adversarial networks","volume":"124","author":"Ren","year":"2020","journal-title":"Robot. Auton. Syst."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"4233","DOI":"10.1109\/LRA.2021.3068671","article-title":"Learning constrained distributions of robot configurations with generative adversarial network","volume":"6","author":"Lembono","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Zhai, J., Zhang, S., Chen, J., and He, Q. (2018, January 7\u201310). Autoencoder and its various variants. Proceedings of the 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Miyazaki, Japan.","DOI":"10.1109\/SMC.2018.00080"},{"key":"ref_65","unstructured":"Kingma, D.P. (2013). Auto-encoding variational bayes. arXiv."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Yoshimitsu, Y., Osa, T., and Ikemoto, S. (2023, January 1\u20135). Forward\/Inverse Kinematics Modeling for Tensegrity Manipulator Based on Goal-Conditioned Variational Autoencoder. Proceedings of the 2023 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Detroit, MI, USA.","DOI":"10.1109\/IROS55552.2023.10341525"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Wilhelm, N., Haddadin, S., Burgkart, R., Van Der Smagt, P., and Karl, M. (2024, January 13\u201317). Accurate Kinematic Modeling using Autoencoders on Differentiable Joints. Proceedings of the 2024 IEEE International Conference on Robotics and Automation (ICRA), Yokohama, Japan.","DOI":"10.1109\/ICRA57147.2024.10611062"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/ACCESS.2023.3234104","article-title":"A deep learning approach to navigating the joint solution space of redundant inverse kinematics and its applications to numerical IK computations","volume":"11","author":"Ho","year":"2023","journal-title":"IEEE Access"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"3964","DOI":"10.1109\/TPAMI.2020.2992934","article-title":"Normalizing flows: An introduction and review of current methods","volume":"43","author":"Kobyzev","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Kim, S., and Perez, J. (June, January 30). Learning reachable manifold and inverse mapping for a redundant robot manipulator. Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi\u2019an, China.","DOI":"10.1109\/ICRA48506.2021.9561589"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"7177","DOI":"10.1109\/LRA.2022.3181374","article-title":"Ikflow: Generating diverse inverse kinematics solutions","volume":"7","author":"Ames","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Park, S., Schwartz, M., and Park, J. (December, January 27). NODEIK: Solving Inverse Kinematics with Neural Ordinary Differential Equations for Path Planning. Proceedings of the 2022 22nd International Conference on Control, Automation and Systems (ICCAS), Busan, Republic of Korea.","DOI":"10.23919\/ICCAS55662.2022.10003852"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","article-title":"The graph neural network model","volume":"20","author":"Scarselli","year":"2008","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Limoyo, O., Mari\u0107, F., Giamou, M., Alexson, P., Petrovi\u0107, I., and Kelly, J. (2023). Euclidean Equivariant Models for Generative Graphical Inverse Kinematics. arXiv.","DOI":"10.1109\/TRO.2024.3521862"},{"key":"ref_75","unstructured":"Limoyo, O., Maric, F., Giamou, M., Alexson, P., Petrovic, I., and Kelly, J. (2022). One Network, Many Robots: Generative Graphical Inverse Kinematics. CoRR."},{"key":"ref_76","unstructured":"Kim, J.T., Park, J., Choi, S., and Ha, S. (2021). Learning robot structure and motion embeddings using graph neural networks. arXiv."},{"key":"ref_77","unstructured":"Sutton, R.S. (2018). Reinforcement learning: An introduction. A Bradford Book, MIT Press."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Malik, A., Lischuk, Y., Henderson, T., and Prazenica, R. (2022). A deep reinforcement-learning approach for inverse kinematics solution of a high degree of freedom robotic manipulator. Robotics, 11.","DOI":"10.3390\/robotics11020044"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Shivkumar, S., and Kumaar, A.N. (2024, January 12\u201314). Manipulator Control using Federated Deep Reinforcement Learning. Proceedings of the 2024 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), Bangalore, India.","DOI":"10.1109\/CONECCT62155.2024.10677205"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Majumder, S., and Sahoo, S.R. (2023, January 18\u201320). A Reinforcement-Learning Approach to Control Robotic Manipulator Based on Improved DDPG. Proceedings of the 2023 Ninth Indian Control Conference (ICC), Visakhapatnam, India.","DOI":"10.1109\/ICC61519.2023.10442503"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Zhao, C., Wei, Y., Xiao, J., Sun, Y., Zhang, D., Guo, Q., and Yang, J. (2024). Inverse kinematics solution and control method of 6-degree-of-freedom manipulator based on deep reinforcement learning. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-62948-6"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1007\/s13042-020-01167-7","article-title":"Multi-agent reinforcement learning for redundant robot control in task-space","volume":"12","author":"Yu","year":"2021","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Chen, Y., Su, S., Ni, K., and Li, C. (2024). Integrated Intelligent Control of Redundant Degrees-of-Freedom Manipulators via the Fusion of Deep Reinforcement Learning and Forward Kinematics Models. Machines, 12.","DOI":"10.3390\/machines12100667"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1109\/THMS.2021.3129708","article-title":"Finite-time observer-based variable impedance control of cable-driven continuum manipulators","volume":"52","author":"Liang","year":"2021","journal-title":"IEEE Trans. Hum.-Mach. Syst."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Wang, Q., Hong, Z., and Zhong, Y. (2022). Learn to swim: Online motion control of an underactuated robotic eel based on deep reinforcement learning. Biomim. Intell. Robot., 2.","DOI":"10.1016\/j.birob.2022.100066"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Hlavac, V. (2021, January 7\u20138). Kinematics control of a redundant planar manipulator with a MLP neural network. Proceedings of the 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Mauritius, Mauritius.","DOI":"10.1109\/ICECCME52200.2021.9591086"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"109","DOI":"10.14311\/NNW.2021.31.006","article-title":"Neural Network for the identification of a functional dependence using data preselection","volume":"2","author":"Hlavac","year":"2021","journal-title":"Neural Netw. World"},{"key":"ref_88","first-page":"202","article-title":"Accuracy of the Inverse Kinematics of a Planar Redundant Manipulator Solved by an MLP Neural Network","volume":"Volume 171","year":"2024","journal-title":"Advances in Mechanism Design IV. TMM 2024. Mechanisms and Machine Science"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"622","DOI":"10.1049\/cit2.12125","article-title":"Recursive recurrent neural network: A novel model for manipulator control with different levels of physical constraints","volume":"8","author":"Li","year":"2023","journal-title":"CAAI Trans. Intell. Technol."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Stephan, B., Dontsov, I., M\u00fcller, S., and Gross, H.M. (2023, January 5\u20138). On Learning of Inverse Kinematics for Highly Redundant Robots with Neural Networks. Proceedings of the 2023 21st International Conference on Advanced Robotics (ICAR), Abu Dhabi, United Arab Emirates.","DOI":"10.1109\/ICAR58858.2023.10406939"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"2854","DOI":"10.1109\/TNNLS.2021.3109953","article-title":"Neural network model-based control for manipulator: An autoencoder perspective","volume":"34","author":"Li","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"103133","DOI":"10.1109\/ACCESS.2024.3432741","article-title":"A deep reinforcement learning framework for control of robotic manipulators in simulated environments","volume":"12","author":"Sarango","year":"2024","journal-title":"IEEE Access"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Blaise, J., and Bazzocchi, M.C. (2023). Space Manipulator Collision Avoidance Using a Deep Reinforcement Learning Control. Aerospace, 10.","DOI":"10.3390\/aerospace10090778"},{"key":"ref_94","unstructured":"Kumar, V., Hoeller, D., Sundaralingam, B., Tremblay, J., and Birchfield, S. (October, January 27). Joint space control via deep reinforcement learning. Proceedings of the RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"V\u00e6hrens, L., \u00c1lvarez, D.D., Berger, U., and B\u00f8gh, S. (2022, January 12\u201314). Learning Task-independent Joint Control for Robotic Manipulators with Reinforcement Learning and Curriculum Learning. Proceedings of the 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA), Nassau, Bahamas.","DOI":"10.1109\/ICMLA55696.2022.00201"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Bengio, Y., Louradour, J., Collobert, R., and Weston, J. (2009, January 14\u201318). Curriculum learning. Proceedings of the 26th Annual International Conference on Machine Learning, Montreal, QC, Canada.","DOI":"10.1145\/1553374.1553380"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1109\/LRA.2021.3116700","article-title":"Sim2real learning of obstacle avoidance for robotic manipulators in uncertain environments","volume":"7","author":"Zhang","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1007\/s10846-023-01822-5","article-title":"An efficiently convergent deep reinforcement learning-based trajectory planning method for manipulators in dynamic environments","volume":"107","author":"Zheng","year":"2023","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"1651","DOI":"10.1017\/S0263574722001898","article-title":"Pythagorean-Hodograph curves-based trajectory planning for pick-and-place operation of Delta robot with prescribed pick and place heights","volume":"41","author":"Su","year":"2023","journal-title":"Robotica"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"108183","DOI":"10.1016\/j.compag.2023.108183","article-title":"A heuristic tomato-bunch harvest manipulator path planning method based on a 3D-CNN-based position posture map and rapidly-exploring random tree","volume":"213","author":"Zhang","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Nocedal, J., and Wright, S.J. (2006). Quadratic programming. Numerical Optimization, Springer.","DOI":"10.1007\/978-0-387-40065-5_18"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"3887","DOI":"10.1109\/TIE.2021.3073305","article-title":"Motion planning of manipulators for simultaneous obstacle avoidance and target tracking: An RNN approach with guaranteed performance","volume":"69","author":"Xu","year":"2021","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"54225","DOI":"10.1109\/ACCESS.2020.2981688","article-title":"Recurrent neural networks-based collision-free motion planning for dual manipulators under multiple constraints","volume":"8","author":"Liang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"14239","DOI":"10.1109\/TII.2024.3441661","article-title":"Whole-Body Inverse Kinematics and Operation-Oriented Motion Planning for Robot Mobile Manipulation","volume":"20","author":"Jin","year":"2024","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Kuang, X., and Zhou, S. (2024). Robotic Manipulator in Dynamic Environment with SAC Combing Attention Mechanism and LSTM. Electronics, 13.","DOI":"10.3390\/electronics13101969"},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Tenhumberg, J., Mielke, A., and B\u00e4uml, B. (2023, January 12\u201314). Efficient Learning of Fast Inverse Kinematics with Collision Avoidance. Proceedings of the 2023 IEEE-RAS 22nd International Conference on Humanoid Robots (Humanoids), Austin, TX, USA.","DOI":"10.1109\/Humanoids57100.2023.10375143"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"5334","DOI":"10.1109\/LRA.2022.3152697","article-title":"Reaching through latent space: From joint statistics to path planning in manipulation","volume":"7","author":"Hung","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Dastider, A., and Lin, M. (2023, January 1\u20135). DAMON: Dynamic Amorphous Obstacle Navigation using Topological Manifold Learning and Variational Autoencoding. Proceedings of the 2023 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Detroit, MI, USA.","DOI":"10.1109\/IROS55552.2023.10342035"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"796","DOI":"10.1109\/TPAMI.2007.70735","article-title":"Riemannian manifold learning","volume":"30","author":"Lin","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"1899","DOI":"10.1007\/s40747-021-00366-1","article-title":"Collision-free path planning for welding manipulator via hybrid algorithm of deep reinforcement learning and inverse kinematics","volume":"8","author":"Zhong","year":"2021","journal-title":"Complex Intell. Syst."},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Ge, D. (2024, January 9\u201311). Research on obstacle avoidance path planning of robotic manipulator based on deep reinforcement learning. Proceedings of the Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), Qingdao, China.","DOI":"10.1117\/12.3049584"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Bhuiyan, T., K\u00e4stner, L., Hu, Y., Kutschank, B., and Lambrecht, J. (2023, January 21\u201323). Deep-reinforcement-learning-based path planning for industrial robots using distance sensors as observation. Proceedings of the 2023 8th International Conference on Control and Robotics Engineering (ICCRE), Niigata, Japan.","DOI":"10.1109\/ICCRE57112.2023.10155608"},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Prianto, E., Park, J.H., Bae, J.H., and Kim, J.S. (2021). Deep reinforcement learning-based path planning for multi-arm manipulators with periodically moving obstacles. Appl. Sci., 11.","DOI":"10.3390\/app11062587"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"4377","DOI":"10.1109\/TMECH.2024.3377002","article-title":"Obstacle-Avoidable Robotic Motion Planning Framework Based on Deep Reinforcement Learning","volume":"29","author":"Liu","year":"2024","journal-title":"IEEE\/ASME Trans. Mech."},{"key":"ref_115","unstructured":"Vaswani, A. (2017, January 4\u20139). Attention is all you need. Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Liu, Y., Liu, S., Liang, W., Wang, C., and Wang, K. (2024). Multimodal Perception for Indoor Mobile Robotics Navigation and Safe Manipulation. IEEE Trans. Cogn. Dev. Syst., 1\u201313.","DOI":"10.1109\/TCDS.2024.3481457"},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Kim, H., Ohmura, Y., and Kuniyoshi, Y. (October, January 27). Transformer-based deep imitation learning for dual-arm robot manipulation. Proceedings of the 2021 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic.","DOI":"10.1109\/IROS51168.2021.9636301"},{"key":"ref_118","unstructured":"Fishman, A., Walsman, A., Bhardwaj, M., Yuan, W., Sundaralingam, B., Boots, B., and Fox, D. (2024, January 9). Avoid Everything: Model-Free Collision Avoidance with Expert-Guided Fine-Tuning. Proceedings of the CoRL Workshop on Safe and Robust Robot Learning for Operation in the Real World, Munich, Germany."},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Huang, X., Batra, D., Rai, A., and Szot, A. (2023, January 1\u20136). Skill transformer: A monolithic policy for mobile manipulation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.00996"},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Xing, D., Xia, W., and Xu, B. (2022, January 23\u201327). Kinematics learning of massive heterogeneous serial robots. Proceedings of the 2022 International Conference on Robotics and Automation (ICRA), Philadelphia, PA, USA.","DOI":"10.1109\/ICRA46639.2022.9812021"},{"key":"ref_121","unstructured":"Alkhodary, A., and Gur, B. (2022). Kinematics transformer: Solving the inverse modeling problem of soft robots using transformers. arXiv."},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Alkhodary, A., and Gur, B. (2023, January 15\u201316). KineFormer: Solving the Inverse Modeling Problem of Soft Robots Using Transformers. Proceedings of the 7th EAI International Conference on Robotic Sensor Networks (ROSENET 2023). EAI\/Springer Innovations in Communication and Computing, Istanbul, T\u00fcrkiye.","DOI":"10.1007\/978-3-031-64495-5_3"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/1\/23\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T10:23:02Z","timestamp":1759918982000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/1\/23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,4]]},"references-count":122,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["a18010023"],"URL":"https:\/\/doi.org\/10.3390\/a18010023","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,4]]}}}