AI-empowered intelligence in industrial robotics: technologies, challenges, and emerging trends

Abstract

Artificial intelligence (AI) is profoundly reshaping the technological framework of industrial robotics, driving its transition from pre-programmed automation to autonomous, adaptive agents. This paper systematically reviews the key advancements of AI across three core dimensions of intelligence: perception, decision-making, and execution. Analysis indicates that AI is propelling industrial robots from tools executing predefined tasks towards intelligent partners capable of adapting to unstructured environments, autonomously planning amid dynamic changes, and engaging in nuanced interactions with the physical world. This evolution reveals a shift from optimizing specific skills towards developing rudimentary task-level cognitive reasoning capabilities. Nevertheless, fundamental challenges persist for industrial-scale deployment, including model generalization capabilities, long-term robustness, and human-machine trust. Collectively, these advancements are shaping a new generation of intelligent industrial robotic systems that are more adaptable and capable of deeper collaboration with humans.

Keywords

Industrial robots / perceptual intelligence / decision-making intelligence / execution intelligence

References

[1] Mo F,Monetti FM.A framework for manufacturing system reconfiguration and optimisation utilising digital twins and modular artificial intelligence.Robot Comput Integr Manuf2023;82:102524

[2] De Winter, J.; Ei Makrini, I.; Van de Perre, G.; Nowé, A.; Verstraten, T.; Vanderborght, B. Autonomous assembly planning of demonstrated skills with reinforcement learning in simulation.Auton Robot2021;45:1097-110

[3] Ibrahim A.Selection of Industry 4.0 technologies for Lean Six Sigma integration using fuzzy DEMATEL approach.Int J Lean Six Sigma2024;15:1025-42

[4] Zafar MH,Sanfilippo F.Exploring the synergies between collaborative robotics, digital twins, augmentation, and industry 5.0 for smart manufacturing: a state-of-the-art review.Robot Comput Integr Manuf2024;89:102769

[5] Soori M,Dastres R.Artificial intelligence, machine learning and deep learning in advanced robotics, a review.Cogn Robot2023;3:54-70

[6] Hao M,Luo X,Yang H.Efficient and privacy-enhanced federated learning for industrial artificial intelligence.IEEE Trans Ind Inf2020;16:6532-42

[7] Velazquez L,Barbalata C.A robotic 3D printer for UV-curable thermosets: dimensionality prediction using a data-driven approach.Int J Comput Integr Manuf2024;37:772-89

[8] Canas-Moreno S,Rios-Navarro A,Perez-Peña F.Towards neuromorphic FPGA-based infrastructures for a robotic arm.Auton Robot2023;47:947-61

[9] Intisar M,Rezaul Islam M.Computer vision based robotic arm controlled using interactive GUI.Intell Autom Soft Comput2021;27:533-50

[10] Cazacu C,Parpală RC,Coteț CE.Optimizing assembly in wiring boxes using API technology for digital twin.Appl Sci2024;14:9483

[11] Ardanza A,Segura Á,Aguinaga D.Sustainable and flexible industrial human machine interfaces to support adaptable applications in the Industry 4.0 paradigm.Int J Prod Res2019;57:4045-59

[12] Tsai Y,Liu T.Utilization of a reinforcement learning algorithm for the accurate alignment of a robotic arm in a complete soft fabric shoe tongues automation process.J Manuf Syst2020;56:501-13

[13] Jeong JH,Kim DJ.Brain-controlled robotic arm system based on multi-directional CNN-BiLSTM network using EEG signals.IEEE Trans Neural Syst Rehabil Eng2020;28:1226-38

[14] Liu J,Yu S.From junk to genius: robotic arms and AI crafting creative designs from scraps.Buildings2024;14:4076

[15] Liu Q,Xu W,Yao B.Knowledge-guided robot learning on compliance control for robotic assembly task with predictive model.Expert Syst Appl2023;234:121037

[16] Cao G.Multi-agent deep reinforcement learning-based robotic arm assembly research.PLoS One2025;20:e0311550

[17] Gao T.Optimizing robotic arm control using deep Q-learning and artificial neural networks through demonstration-based methodologies: a case study of dynamic and static conditions.Robot Auton Syst2024;181:104771

[18] Li T,Li J.An adaptive control method and learning strategy for ultrasound-guided puncture robot.Electronics2024;13:580

[19] Kumar A.Linguistic Lyapunov reinforcement learning control for robotic manipulators.Neurocomputing2018;272:84-95

[20] Juarez-Lora A,Sossa H.R-STDP spiking neural network architecture for motion control on a changing friction joint robotic arm.Front Neurorobot2022;16:904017 PMCID:PMC9161736

[21] Guo C.FPGA-accelerated sim-to-real control policy learning for robotic arms.IEEE Trans Circuits Syst II2024;71:1690-4

[22] Katona K,Korondi P.Obstacle avoidance and path planning methods for autonomous navigation of mobile robot.Sensors2024;24:3573 PMCID:PMC11175283

[23] Ušinskis V,Dzedzickis A.Sensor-fusion based navigation for autonomous mobile robot.Sensors2025;25:1248 PMCID:PMC11861736

[24] Eren B,Mistikoglu S.Recent developments in computer vision and artificial intelligence aided intelligent robotic welding applications.Int J Adv Manuf Technol2023;126:4763-809

[25] Fu J,Li S.Recent advancements in augmented reality for robotic applications: a survey.Actuators2023;12:323

[26] Rho E,Mun J,Cho K.Impact of physical parameters and vision data on deep learning-based grip force estimation for fluidic origami soft grippers.IEEE Robot Autom Lett2024;9:2487-94

[27] Yang C,Eom D.Enhancing ToF sensor precision using 3D models and simulation for vision inspection in industrial mobile robots.Appl Sci2024;14:4595

[28] Mena-Almonte RA,Etxeberria-Agiriano I.Efficient robot localization through deep learning-based natural fiduciary pattern recognition.Mathematics2025;13:467

[29] Wu H,Lu W,Bao J.Automated part placement for precast concrete component manufacturing: an intelligent robotic system using target detection and path planning.J Comput Civ Eng2025;39:04024044

[30] Nguyen V,Su S,Bui T.Vision-based pick and place control system for industrial robots using an eye-in-hand camera.IEEE Access2025;13:25127-40

[31] Wei D,Gu Y.Robot grasp in cluttered scene using a multi-stage deep learning model.IEEE Robot Autom Lett2024;9:6512-9

[32] Huang C,Shao Y,Yang S.Rapid-learning collaborative pushing and grasping via deep reinforcement learning and image masking.Appl Sci2024;14:9018

[33] Wang S,Zhou L,Liu W.3DWDC-Net: an improved 3DCNN with separable structure and global attention for weld internal defect classification based on phased array ultrasonic tomography images.Mech Syst Signal Process2025;229:112564

[34] Hassan SA,Radziwon A,Oddo CM.Textile fabric defect detection using enhanced deep convolutional neural network with safe human–robot collaborative interaction.Electronics2024;13:4314

[35] Zhou S,Jiang L.RoboCam: model-based robotic visual sensing for precise inspection of mesh screens.ACM Trans Sens Netw2025;21:1-23

[36] Rocha F,Pereira RFS.ROSI: a robotic system for harsh outdoor industrial inspection - system design and applications.J Intell Robot Syst2021;103:1459

[37] Surindra MD,Caesarendra W.Use of machine learning models in condition monitoring of abrasive belt in robotic arm grinding process.J Intell Manuf2025;36:3345-58

[38] Chew SY,Vargas-Uscategui A.In-process 4D reconstruction in robotic additive manufacturing.Robot Comput Integr Manuf2024;89:102784

[39] Ni H,Deng J,Luo S.Digital twin-driven virtual commissioning for robotic machining enhanced by machine learning.Robot Comput Integr Manuf2025;93:102908

[40] Chen Y.An intuitive pre-processing method based on human–robot interactions: zero-shot learning semantic segmentation based on synthetic semantic template.J Supercomput2023;79:11743-66

[41] Ghafarian Tamizi, M.; Honari, H.; Nozdryn-Plotnicki, A.; Najjaran, H. End-to-end deep learning-based framework for path planning and collision checking: bin-picking application.Robotica2024;42:1094-112

[42] Arents J.Smart industrial robot control trends, challenges and opportunities within manufacturing.Appl Sci2022;12:937

[43] De Roovere, P.; Moonen, S.; Michiels, N.; wyffels, F. Sim-to-real dataset of industrial metal objects.Machines2024;12:99

[44] Lee J,Cheng E,Hsieh C.Intelligent robotic palletizer system.Appl Sci2021;11:12159

[45] Hou R,Liu Y.Research on multi-hole localization tracking based on a combination of machine vision and deep learning.Sensors2024;24:984 PMCID:PMC10857067

[46] Song Y,Fei Y. Deep robotic prediction with hierarchical RGB-D fusion. arXiv 2019, arXiv:1909.06585. Available online: https://doi.org/10.48550/arXiv.1909.06585. (accessed 3 December 2025)

[47] Lin C,Shih C.Vision-based robotic arm control for screwdriver bit placement tasks.Sens Mater2024;36:1003

[48] Lee SKH,Hinchy EP,O’dowd NP.A vision-based hole quality assessment technique for robotic drilling of composite materials using a hybrid classification model.Int J Adv Manuf Technol2023;129:1249-58

[49] Comari S.Autonomous scanning and cleanliness classification of pharmaceutical bins through artificial intelligence and robotics.IEEE Access2024;12:117256-70

[50] Kohut P.Vision systems for a UR5 cobot on a quality control robotic station.Appl Sci2024;14:9469

[51] Cheng A,Gao F.Anomaly detection of tire tiny text: mechanism and method.IEEE Trans Autom Sci Eng2024;21:1911-28

[52] Lin C,Gao Y.Vision-based robotic arm in defect detection and object classification applications.Sens Mater2024;36:655

[53] Pereira FR,Souza HDSE.Force and vision-based system for robotic sealing monitoring.Int J Adv Manuf Technol2023;126:391-403

[54] Mukherjee D,Chang LH.A survey of robot learning strategies for human-robot collaboration in industrial settings.Robot Comput Integr Manuf2022;73:102231

[55] Mendez E,Olivera-Guzman D.Integration of deep learning and collaborative robot for assembly tasks.Appl Sci2024;14:839

[56] Bartyzel G.Multimodal variational DeepMDP: an efficient approach for industrial assembly in high-mix, low-volume production.IEEE Robot Autom Lett2024;9:11297-304

[57] Li T,Lou R,Nozais D.Machine learning-based 3D scan coverage prediction for smart-control applications.Comput Aided Des2024;176:103775

[58] Roos-Hoefgeest S,Alvarez I. Reinforcement learning approach to optimizing profilometric sensor trajectories for surface inspection. arXiv 2024, arXiv:2409.03429. Available online: https://doi.org/10.48550/arXiv.2409.03429. (accessed 3 December 2025)

[59] Simoni A,Garattoni L,Vezzani R.D-SPDH: improving 3D robot pose estimation in Sim2Real scenario via depth data.IEEE Access2024;12:166660-73

[60] Mangat AS,Rinderle-Ma S.Interactive process automation based on lightweight object detection in manufacturing processes.Comput Ind2021;130:103482

[61] Gao Z,Chong NY.Zero moment two edge pushing of novel objects with center of mass estimation.IEEE Trans Autom Sci Eng2023;20:1487-99

[62] Celikel R.NARMA-L2 controller for single link manipulator. In 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), Malatya, Turkey, September 28-30, 2018. IEEE; 2018. p. 1-6.

[63] Faroni M,Beschi M,Cesta A.Optimal task and motion planning and execution for multiagent systems in dynamic environments.IEEE Trans Cybern2024;54:3366-77

[64] Calderón-Cordova C,Castillo D.A deep reinforcement learning framework for control of robotic manipulators in simulated environments.IEEE Access2024;12:103133-61

[65] Wu P,Dong H,Li M.An obstacle avoidance method for robotic arm based on reinforcement learning.Ind Robot2025;52:9-17

[66] Lindner T.Reinforcement learning-based algorithm to avoid obstacles by the anthropomorphic robotic arm.Appl Sci2022;12:6629

[67] Liu Q,Xiong B,Liu Y.Deep reinforcement learning-based safe interaction for industrial human-robot collaboration using intrinsic reward function.Adv Eng Inform2021;49:101360

[68] Honelign L,Tullu A.Deep reinforcement learning-based enhancement of robotic arm target-reaching performance.Actuators2025;14:165

[69] Ji Z,Xu W,Liu X.Deep reinforcement learning on variable stiffness compliant control for programming-free robotic assembly in smart manufacturing.Int J Prod Res2024;62:7073-95

[70] Men Y,Cui T,Li F.Policy fusion transfer: the knowledge transfer for different robot peg-in-hole insertion assemblies.IEEE Trans Instrum Meas2023;72:1-10

[71] Zhou H.Intelligent redundant manipulation for long-horizon operations with multiple goal-conditioned hierarchical learning.Adv Robot2025;39:291-304

[72] Koubaa A,Boulila W.Next-generation human-robot interaction with ChatGPT and robot operating system.Softw Pract Exp2025;55:355-82

[73] Gupta S,Niederhauser L.Action contextualization: adaptive task planning and action tuning using large language models.IEEE Robot Autom Lett2024;9:9407-14

[74] Hou W,Yue M.Human-robot collaborative assembly task planning for mobile cobots based on deep reinforcement learning.Proc Inst Mech Eng C2024;238:11097-114

[75] Angelidis A,Vosniakos G.An open extended reality platform supporting dynamic robot paths for studying human–robot collaboration in manufacturing.Int J Adv Manuf Technol2025;138:3-15

[76] Zhao D,Li W,Du Y.Cascaded fuzzy reward mechanisms in deep reinforcement learning for comprehensive path planning in textile robotic systems.Appl Sci2024;14:851

[77] Lin J,Xian W.Continuous reinforcement learning via advantage value difference reward shaping: a proximal policy optimization perspective.Eng Appl Artif Intell2025;151:110676

[78] Bing Z,Li R.Solving robotic manipulation with sparse reward reinforcement learning via graph-based diversity and proximity.IEEE Trans Ind Electron2023;70:2759-69

[79] Yu C,Cheng Y,Shi M.Trajectory tracking control of an unmanned aerial vehicle with deep reinforcement learning for tasks inside the EAST.Fusion Eng Des2023;194:113894

[80] Bartyzel G,Rzepka D.Reinforcement learning with stereo-view observation for robust electronic component robotic insertion.J Intell Robot Syst2023;109:1970

[81] Yu S.Inverse kinematics of a 7-degree-of-freedom robotic arm based on deep reinforcement learning and damped least squares.IEEE Access2025;13:4857-68

[82] Amirnia A.Real-time sustainable cobotic disassembly planning using fuzzy reinforcement learning.Int J Prod Res2025;63:3798-821

[83] Tian B,Janardhanan M.Balancing heterogeneous assembly line with multi-skilled human-robot collaboration via adaptive cooperative co-evolutionary algorithm.Swarm Evol Comput2024;91:101762

[84] Wang T,Zheng P.An LLM-based vision and language cobot navigation approach for human-centric smart manufacturing.J Manuf Syst2024;75:299-305

[85] Li C,Zhang X.IRWoZ: constructing an industrial robot Wizard-of-OZ dialoguing dataset.IEEE Access2023;11:28236-51

[86] Li C,Chrysostomou D.ToD4IR: a humanised task-oriented dialogue system for industrial robots.IEEE Access2022;10:91631-49

[87] Čakurda T,Pomin P.Deep learning methods in soft robotics: architectures and applications.Adv Intell Syst2025;7:2400576

[88] Bilancia P,Tutarini A,Iori M.Online motion accuracy compensation of industrial servomechanisms using machine learning approaches.Robot Comput Integr Manuf2025;91:102838

[89] Bi Z,Miao Z,Zhang W.Safety assurance mechanisms of collaborative robotic systems in manufacturing.Robot Comput Integr Manuf2021;67:102022

[90] Shan S.Fine robotic manipulation without force/torque sensor.IEEE Robot Autom Lett2024;9:1206-13

[91] Deng W,Nguyen KT,Medjaher K.Physics informed machine learning model for inverse dynamics in robotic manipulators.Appl Soft Comput2024;163:111877

[92] Zhou H,Wang G,Liu Z.A hybrid control strategy for grinding and polishing robot based on adaptive impedance control.Adv Mech Eng2021;13:168781402110040

[93] Ma H,Li Z,Wu X.Research on 3C compliant assembly strategy method of manipulator based on deep reinforcement learning.Comput Electr Eng2024;119:109605

[94] Zhang S,Liang S,Jiang Z.Research on robotic peg-in-hole assembly method based on variable admittance.Appl Sci2025;15:2143

[95] Hu X,Ren P.An admittance parameter optimization method based on reinforcement learning for robot force control.Actuators2024;13:354

[96] Zheyuan C,Tao H,Pengxuan D.Need for developing a security robot-based risk management for emerging practices in the workplace using the advanced human-robot collaboration model.Work2021;68:825-34

[97] Xin X,Sevegnani M,Khoo TP.Adaptive model verification for modularized industry 4.0 applications.IEEE Access2022;10:125353-64

[98] Hickman X,Prince D.Hybrid safe reinforcement learning: tackling distribution shift and outliers with the Student-t’s process.Neurocomputing2025;634:129912

[99] Kana S,Mohan DM.Impedance controlled human–robot collaborative tooling for edge chamfering and polishing applications.Robot Comput Integr Manuf2021;72:102199

[100] Amaya C.Neurorobotic reinforcement learning for domains with parametrical uncertainty.Front Neurorobot2023;17:1239581 PMCID:PMC10642204

[101] Mahdi MM,Noh SD.Digital twin-based architecture for wire arc additive manufacturing using OPC UA.Robot Comput Integr Manuf2025;94:102944

[102] Li C,Li S,Lee CK.AR-assisted digital twin-enabled robot collaborative manufacturing system with human-in-the-loop.Robot Comput Integr Manuf2022;76:102321

[103] Zhang T,Lin J,Huang H.Sim2real learning of obstacle avoidance for robotic manipulators in uncertain environments.IEEE Robot Autom Lett2022;7:65-72

[104] Zhang Z,Wang L,Huang H.Digital twin-enabled grasp outcomes assessment for unknown objects using visual-tactile fusion perception.Robot Comput Integr Manuf2023;84:102601

[105] Liu Y,Liu D.A digital twin-based sim-to-real transfer for deep reinforcement learning-enabled industrial robot grasping.Robot Comput Integr Manuf2022;78:102365

[106] Wang R,Kashima K.Density estimation based soft actor-critic: deep reinforcement learning for static output feedback control with measurement noise.Adv Robot2024;38:398-409

[107] Salehi A,Doncieux S.Adaptive asynchronous control using meta-learned neural ordinary differential equations.IEEE Trans Robot2024;40:403-20

[108] Shin G,Kim W.A novel policy distillation with WPA-based knowledge filtering algorithm for efficient industrial robot control.IEEE Access2024;12:154514-25

[109] Wang S,Jiang Q,Liu C.Manipulator joint fault localization for intelligent flexible manufacturing based on reinforcement learning and robot dynamics.Robot Comput Integr Manuf2024;86:102684

[110] Kim H.Robot dynamics-based cable fault diagnosis using stacked transformer encoder layers.Electr Eng2025;107:3697-708

[111] Lanese I,Ulidowski I.Reversible execution for robustness in embodied AI and industrial robots.IT Prof2021;23:12-7

[112] Hsieh Y,Lin S.Deep convolutional generative adversarial network for inverse kinematics of self-assembly robotic arm based on the depth sensor.IEEE Sens J2023;23:758-65