Humanoid robots are increasingly being explored for use in human-centered environments, particularly in healthcare, where aging populations, workforce shortages, and growing demands for physical assistance present ongoing challenges. Despite the recent advances in artificial intelligence, sensing, and actuation, there remains a gap between experimental performance and reliable deployment in real-world settings. This review examines the current state of humanoid robotics with an emphasis on applications requiring physical interaction and adaptability. Relevant literature was analyzed to evaluate progress in sensory-motor integration, locomotion, manipulation, and autonomy, along with key factors influencing human-robot interaction, including trust, safety, and ethical considerations. Current evidence suggests that humanoid robots demonstrate strong performance in controlled environments and show promise in rehabilitative and assistive roles. However, limitations in generalizability, robustness, and long-term reliability continue to restrict their use in unstructured, real-world contexts. In healthcare settings, these constraints are further compounded by high safety requirements and the need for consistent, predictable behavior. Overall, the current literature indicates that near-term deployment is most feasible through gradual, task-specific integration and hybrid human-robot collaboration rather than full autonomy. Humanoid robots are more likely to augment human capabilities than replace them, specifically in repetitive or physically demanding tasks. Future progress will depend not only on technical improvements but also on ethical oversight, regulatory development, and alignment of system capabilities with user expectations.
References
[1]
Gu, Z., Li, J., Shen, W., Yu, W., Xie, Z., McCrory, S., et al. (2025) Humanoid Locomotion and Manipulation: Current Progress and Challenges in Control, Planning, and Learning. arXiv: 2501.02116. https://arxiv.org/abs/2501.02116
[2]
Song, X., Li, Y., Zhang, Y., Liu, Y. and Jiang, L. (2025) An Overview of Learning-Based Dexterous Grasping: Recent Advances and Future Directions. ArtificialIntelligenceReview, 58, Article No. 300. https://doi.org/10.1007/s10462-025-11262-2
[3]
Kim, M., Jeong, H., Kantharaju, P., Yoo, D., Jacobson, M., Shin, D., et al. (2022) Visual Guidance Can Help with the Use of a Robotic Exoskeleton during Human Walking. ScientificReports, 12, Article No. 3881. https://doi.org/10.1038/s41598-022-07736-w
[4]
Pérez-Zuñiga, G., Arce, D., Gibaja, S., Alvites, M., Cano, C., Bustamante, M., et al. (2024) Qhali: A Humanoid Robot for Assisting in Mental Health Treatment. Sensors, 24, Article 1321. https://doi.org/10.3390/s24041321
[5]
Kóczi, D. and Sárosi, J. (2025) Safety Engineering for Humanoid Robots in Everyday Life—Scoping Review. Electronics, 14, Article 4734. https://doi.org/10.3390/electronics14234734
[6]
Roychoudhury, A., Khorshidi, S., Agrawal, S. and Bennewitz, M. (2023) Perception for Humanoid Robots. CurrentRoboticsReports, 4, 127-140. https://doi.org/10.1007/s43154-023-00107-x
[7]
Kanehiro, F., Suleiman, W. and Griffin, R. (2022) Editorial: Humanoid Robots for Real-World Applications. FrontiersinRoboticsandAI, 9, Article 938775. https://doi.org/10.3389/frobt.2022.938775
[8]
Sciutti, A., Mara, M., Tagliasco, V. and Sandini, G. (2018) Humanizing Human-Robot Interaction: On the Importance of Mutual Understanding. IEEE Technology and Society Magazine, 37, 22-29.
[9]
Sivasubramani, S. (2024) The Rise of Humanoid Robots. IEEE Transmitter. https://transmitter.ieee.org/the-rise-of-humanoid-robots/
[10]
Yang, Y. (2025) Research and Application of Robot Vision Sensors. AppliedandComputationalEngineering, 184, 123-131. https://doi.org/10.54254/2755-2721/2025.ld27260
[11]
Su, H., Qi, W., Chen, J., Yang, C., Sandoval, J. and Laribi, M.A. (2023) Recent Advancements in Multimodal Human-Robot Interaction. FrontiersinNeurorobotics, 17, Article 1084000. https://doi.org/10.3389/fnbot.2023.1084000
[12]
Massari, L., Oddo, C.M., Sinibaldi, E., Detry, R., Bowkett, J. and Carpenter, K.C. (2019) Tactile Sensing and Control of Robotic Manipulator Integrating Fiber Bragg Grating Strain-Sensor. Frontiers in Neurorobotics, 13, Article 8. https://doi.org/10.3389/fnbot.2019.00008
[13]
Riener, R., Rabezzana, L. and Zimmermann, Y. (2023) Do Robots Outperform Humans in Human-Centered Domains? Frontiers in Robotics and AI, 10, Article 1223946. https://doi.org/10.3389/frobt.2023.1223946
[14]
Vicente, P., Jamone, L. and Bernardino, A. (2016) Online Body Schema Adaptation Based on Internal Mental Simulation and Multisensory Feedback. Frontiers in Robotics and AI, 3, Article 7. https://doi.org/10.3389/frobt.2016.00007
[15]
Zhao, W., Gangaraju, K. and Yuan, F. (2025) Multimodal Perception-Driven Decision-Making for Human-Robot Interaction: A Survey. Frontiers in Robotics and AI, 12, Article 1604472. https://doi.org/10.3389/frobt.2025.1604472
[16]
Jiang, Z., Wang, Y., Wang, S., Bi, S. and Chen, J. (2024) Motion Planning and Control with Environmental Uncertainties for Humanoid Robot. Sensors, 24, Article 7652. https://doi.org/10.3390/s24237652
[17]
Han, X. (2026) LG-H-PPO: Offline Hierarchical PPO for Robot Path Planning on a Latent Graph. Frontiers in Robotics and AI, 12, Article 1737238. https://doi.org/10.3389/frobt.2025.1737238
[18]
Kober, J., Bagnell, J.A. and Peters, J. (2013) Reinforcement Learning in Robotics: A Survey. The International Journal of Robotics Research, 32, 1238-1274. https://doi.org/10.1177/0278364913495721
[19]
Da Costa, L., Lanillos, P., Sajid, N., Friston, K. and Khan, S. (2022) How Active Inference Could Help Revolutionise Robotics. Entropy, 24, Article 361. https://doi.org/10.3390/e24030361
[20]
Escobar-Juárez, E., Schillaci, G., Hermosillo-Valadez, J. and Lara-Guzmán, B. (2016) A Self-Organized Internal Models Architecture for Coding Sensory-Motor Schemes. FrontiersinRoboticsandAI, 3, Article 22. https://doi.org/10.3389/frobt.2016.00022
[21]
Sutton, R.S. and Barto, A.G. (2018) Reinforcement Learning: An Introduction. 2nd Edition, MIT Press.
[22]
Meng, J., Xiao, H., Jiang, L., Hu, Z., Jiang, L. and Jiang, N. (2023) Adaptive Model Predictive Control for Mobile Robots with Localization Fluctuation Estimation. Sensors, 23, Article 2501. https://doi.org/10.3390/s23052501
[23]
Siva, S. and Zhang, H. (2022) Robot Perceptual Adaptation to Environment Changes for Long-Term Human Teammate Following. The International Journal of Robotics Research, 41, 706-720. https://doi.org/10.1177/0278364919896625
[24]
Dellaert, F. and Hutchinson, S. (2023) 1.2 Reasoning. Introduction to Robotics and Perception. https://www.roboticsbook.org/S12_reasoning.html
[25]
Albustanji, R., Elmanaseer, S. and Alkhatib, A. (2023) Robotics: Five Senses Plus One—An Overview. Robotics, 12, Article 68. https://doi.org/10.3390/robotics12030068
[26]
Sasikala, D., Suresh Kumar, K., Raja, M.S., Anusuya, S., Ganesh Babu, L. and Girimurugan, R. (2026) Sensor Fusion and Data Processing for Robot Control. In: Sountharrajan, S., Karthiga, M., Balusamy, B. and Bashir, A.K., Eds., Applied Mathematical Modeling for BiomedicalRoboticsandWearableDevices, Elsevier, 117-134. https://doi.org/10.1016/b978-0-443-33514-3.00007-1
[27]
Jin, P. (2018) Uncertainty Adaptation in Robot Perception and Learning. CMU-CS-18-103. Carnegie Mellon University. http://reports-archive.adm.cs.cmu.edu/anon/2018/CMU-CS-18-103.pdf
[28]
Dautenhahn, K. (2007) Socially Intelligent Robots: Dimensions of Human-Robot Interaction. PhilosophicalTransactionsoftheRoyalSocietyB: BiologicalSciences, 362, 679-704. https://doi.org/10.1098/rstb.2006.2004
[29]
Lasota, P.A. and Shah, J.A. (2015) Analyzing the Effects of Human-Aware Motion Planning on Close-Proximity Human-Robot Collaboration. Human Factors: The Journal oftheHumanFactorsandErgonomicsSociety, 57, 21-33. https://doi.org/10.1177/0018720814565188
[30]
Oehlke, J., Beckerle, P., Seyfarth, A. and Sharbafi, M.A. (2019) Human-Like Hopping in Machines: Feedback-Versus Feed-Forward-Controlled Motions. Biological Cybernetics, 113, 227-238. https://doi.org/10.1007/s00422-018-0788-4
[31]
Signa, A., Chella, A. and Gentile, M. (2021) Cognitive Robots and the Conscious Mind: A Review of the Global Workspace Theory. CurrentRoboticsReports, 2, 125-131. https://doi.org/10.1007/s43154-021-00044-7
[32]
Rodriguez-Guerra, D., Sorrosal, G., Cabanes, I. and Calleja, C. (2021) Human-Robot Interaction Review: Challenges and Solutions for Modern Industrial Environments. IEEE Access, 9, 108557-108578. https://doi.org/10.1109/access.2021.3099287
[33]
Hancock, P.A., Kessler, T.T., Kaplan, A.D., Stowers, K., Brill, J.C., Billings, D.R., et al. (2023) How and Why Humans Trust: A Meta-Analysis and Elaborated Model. FrontiersinPsychology, 14, Article 1081086. https://doi.org/10.3389/fpsyg.2023.1081086
[34]
Yanco, H.A. and Drury, J. (2004) Classifying Human-Robot Interaction: An Updated Taxonomy. 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583), The Hague, 10-13 October 2004, 2841-2846. https://doi.org/10.1109/icsmc.2004.1400763
[35]
Beer, J.M., Fisk, A.D. and Rogers, W.A. (2014) Toward a Framework for Levels of Robot Autonomy in Human-Robot Interaction. Journal of Human-Robot Interaction, 3, 74-99. https://doi.org/10.5898/jhri.3.2.beer
[36]
Pelikan, H.R.M. and Broth, M. (2016) Why That Nao? How Humans Adapt to a Conventional Humanoid Robot in Taking Turns-At-Talk. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, San Jose, 7-12 May 2016, 4921-4932. https://doi.org/10.1145/2858036.2858478
[37]
Pipitone, A., Geraci, A., D’Amico, A., Seidita, V. and Chella, A. (2024) Robot’s Inner Speech Effects on Human Trust and Anthropomorphism. International Journal of Social Robotics, 16, 1333-1345. https://doi.org/10.1007/s12369-023-01002-3
[38]
Saeki, W. and Ueda, Y. (2024) Sequential Model Based on Human Cognitive Processing to Robot Acceptance. FrontiersinRoboticsandAI, 11, Article 1362044. https://doi.org/10.3389/frobt.2024.1362044
[39]
Chauhan, S., Kapoor, S., Nagpal, M., Choudhary, G. and Dutt, V. (2025) Building Trust in the Age of Human-Machine Interaction: Insights, Challenges, and Future Directions. FrontiersinRoboticsandAI, 12, Article 1535082. https://doi.org/10.3389/frobt.2025.1535082
[40]
Wullenkord, R. and Eyssel, F. (2020) Societal and Ethical Issues in Hri. CurrentRoboticsReports, 1, 85-96. https://doi.org/10.1007/s43154-020-00010-9
[41]
Sharkey, A. and Sharkey, N. (2012) Granny and the Robots: Ethical Issues in Robot Care for the Elderly. EthicsandInformationTechnology, 14, 27-40. https://doi.org/10.1007/s10676-010-9234-6
[42]
Çetin, E. (2024) Public Perceptions and Acceptance of Artificial Intelligence Humanoid Bots/Robots: Evidence from Turkey. Technological Forecasting and Social Change, 208, Article ID: 123678. https://doi.org/10.1016/j.techfore.2024.123678
[43]
Malle, B., Fischer, K., Young, J., Moon, A.J. and Collins, E. (2020) Trust and the Discrepancy between Expectations and Actual Capabilities of Social Robots. In: Zhang, D. and Wei, B., Eds., Human-Robot Interaction: Control, Analysis, and Design, Cambridge Scholars Publishing, 1-23.
[44]
Yang, G., J. Nelson, B., Murphy, R.R., Choset, H., Christensen, H., H. Collins, S., et al. (2020) Combating Covid-19—The Role of Robotics in Managing Public Health and Infectious Diseases. ScienceRobotics, 5, eabb5589. https://doi.org/10.1126/scirobotics.abb5589
[45]
Li, M., Liu, X., Gao, Y., Sun, Y., Li, P., Zhou, L., et al. (2026) Application Management and Effectiveness Analysis of Intelligent Logistics Robots in Hospital Drug and Specimen Delivery Scenarios. ScientificReports. https://doi.org/10.1038/s41598-026-49800-9
[46]
Abbas, G.H., Speksnijder, C., Ramnarain, D., Parmer, C., Parmar, A., Ahmad, S., et al. (2025) Ai-driven Rehabilitation Robotics: Advancements in and Impacts on Patient Recovery. Cureus, 17, e94273. https://doi.org/10.7759/cureus.94273
[47]
Tong, Y., Liu, H. and Zhang, Z. (2024) Advancements in Humanoid Robots: A Comprehensive Review and Future Prospects. IEEE/CAAJournalofAutomaticaSinica, 11, 301-328. https://doi.org/10.1109/jas.2023.124140
[48]
Mennella, C., Maniscalco, U., De Pietro, G. and Esposito, M. (2024) Ethical and Regulatory Challenges of AI Technologies in Healthcare: A Narrative Review. Heliyon, 10, e26297. https://doi.org/10.1016/j.heliyon.2024.e26297
[49]
Siciliano, B. and Khatib, O. (2016) Springer Handbook of Robotics. 2nd Edition, Springer. https://doi.org/10.1007/978-3-319-32552-1
[50]
Cordella, F., Farina, D. and Zollo, L. (2025) Editorial: Human-In-The-Loop Paradigm for Assistive Robotics. FrontiersinRoboticsandAI, 12, Article 1718326. https://doi.org/10.3389/frobt.2025.1718326
[51]
Ackerman, E. (2025) Reality Is Ruining the Humanoid Robot Hype. IEEE Spectrum. https://spectrum.ieee.org/humanoid-robot-scaling
[52]
Andtfolk, M., Nyholm, L., Eide, H., Rauhala, A. and Fagerström, L. (2022) Attitudes toward the Use of Humanoid Robots in Healthcare—A Cross-Sectional Study. AI&SOCIETY, 37, 1739-1748. https://doi.org/10.1007/s00146-021-01271-4
[53]
Mlakar, I., Kampič, T., Flis, V., Kobilica, N., Molan, M., Smrke, U., et al. (2022) Study Protocol: A Survey Exploring Patients’ and Healthcare Professionals’ Expectations, Attitudes and Ethical Acceptability Regarding the Integration of Socially Assistive Humanoid Robots in Nursing. BMJOpen, 12, e054310. https://doi.org/10.1136/bmjopen-2021-054310
[54]
Latikka, R., Turja, T. and Oksanen, A. (2019) Self-Efficacy and Acceptance of Robots. ComputersinHumanBehavior, 93, 157-163. https://doi.org/10.1016/j.chb.2018.12.017
[55]
Mlakar, I., Roj, I.R., Flis, V., Šafran, V., Smrke, U. and Plohl, N. (2025) Facilitating Acceptance, Trust, and Ethical Integration of Socially Assistive Robots among Nurses: A Quasi-Experimental Study. HealthPolicyandTechnology, 14, Article ID: 101034. https://doi.org/10.1016/j.hlpt.2025.101034
Uthai, T., You, H., Wang, M., Smith, K., Spackman, E., Ryan, Z., et al. (2025) Opportunities Challenges and Roadmap for Humanoid Robots in Construction. ScientificReports, 16, Article No. 905. https://doi.org/10.1038/s41598-025-30252-6
[58]
Warmbein, A., Rathgeber, I., Seif, J., Mehler-Klamt, A.C., Schmidbauer, L., Scharf, C., et al. (2023) Barriers and Facilitators in the Implementation of Mobilization Robots in Hospitals from the Perspective of Clinical Experts and Developers. BMCNursing, 22, Article No. 45. https://doi.org/10.1186/s12912-023-01202-2
[59]
Papadopoulos, I., Koulouglioti, C., Lazzarino, R. and Ali, S. (2020) Enablers and Barriers to the Implementation of Socially Assistive Humanoid Robots in Health and Social Care: A Systematic Review. BMJOpen, 10, e033096. https://doi.org/10.1136/bmjopen-2019-033096
[60]
Firmino de Souza, D., Sousa, S., Kristjuhan-Ling, K., Dunajeva, O., Roosileht, M., Pentel, A., et al. (2025) Trust and Trustworthiness from Human-Centered Perspective in Human-Robot Interaction (HRI)—A Systematic Literature Review. Electronics, 14, Article 1557. https://doi.org/10.3390/electronics14081557
[61]
De Santis, A., Siciliano, B., De Luca, A. and Bicchi, A. (2008) An Atlas of Physical Human-Robot Interaction. MechanismandMachineTheory, 43, 253-270. https://doi.org/10.1016/j.mechmachtheory.2007.03.003
[62]
Deo, N. and Anjankar, A. (2023) Artificial Intelligence with Robotics in Healthcare: A Narrative Review of Its Viability in India. Cureus, 15, e39416. https://doi.org/10.7759/cureus.39416
[63]
Laban, G., Kappas, A., Morrison, V. and Cross, E.S. (2024) Building Long-Term Human-Robot Relationships: Examining Disclosure, Perception and Well-Being across Time. International JournalofSocialRobotics, 16, 1-27. https://doi.org/10.1007/s12369-023-01076-z
