A systematic evaluation shows that contemporary humanoid robots can perform laparoscopic surgical tasks through teleoperation, highlighting both their promise and key technical challenges before clinical deployment.
Abstract
Recent advances in actuation, control and learning have rapidly pushed humanoid robots from a distant vision towards near-term real-world deployment1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17–18. Healthcare is a particularly pressing domain, in which staffing shortages and increasing care demand are widening the gap between clinical workload and available skilled labour19, 20–21. Although current automation has largely focused on digital and logistical tasks22, much hospital work remains embodied, requiring mobility, manipulation and safe interaction in human-designed environments. Humanoid form factors offer unique potential, particularly for assisting with surgical tasks. Traditionally, robotic systems for surgery are purpose-built platforms such as Intuitive Surgical’s da Vinci Surgical System23,24, and it remains unclear how close current humanoid systems are to meeting the precision, control and safety requirements of minimally invasive surgery. Here we present a systematic evaluation of contemporary humanoid technology for laparoscopic surgical tasks. We develop a humanoid-based laparoscopic teleoperation framework using general-purpose instruments and assess its abilities through benchtop characterization, dry-laboratory user studies spanning diverse surgical experience levels and in vivo porcine studies. Across these evaluations, we quantify technical feasibility, task performance and clinical readiness relative to established surgical platforms. Together, our study provides an evidence-based assessment of current humanoid abilities and limitations for surgical applications, highlighting both their promise and key technical challenges that must be addressed before clinical deployment. A systematic evaluation shows that contemporary humanoid robots can perform laparoscopic surgical tasks through teleoperation, highlighting both their promise and key technical challenges before clinical deployment.
The increasing demand for high-quality surgical care, together with the uneven distribution of experienced surgeons and advanced medical infrastructure, has motivated growing interest in AI-enabled surgical robotics. Recent advances in machine learning, multimodal foundation models, embodied intelligence, and robotic control have created new opportunities for improving perception, navigation, planning, and task-specific automation in surgical systems. However, the current evidence base remains heterogeneous: many reported capabilities have been demonstrated only in simulation, phantom models, ex vivo experiments, animal studies, or narrowly defined clinical scenarios. Therefore, claims regarding clinical outcome improvement, scalability, and healthcare equity require careful interpretation and further validation. This paper presents a review of AI-assisted and autonomous surgical robotics. We summarize recent progress across three major technical dimensions: surgical information acquisition and scene understanding, autonomous motion synthesis and control, and simulation-based validation platforms, including emerging world-model approaches. We also review foundational concepts such as levels of surgical autonomy, surgical navigation, robotic platforms, and representative AI architectures. Particular attention is given to distinguishing demonstrated capabilities from translational potential and long-term visions of autonomous surgery. The review suggests that AI-enabled surgical systems have shown promise in selected tasks, including surgical phase recognition, instrument and tissue perception, robotic skill learning, suturing, tracking, and simulation-based policy validation. Nevertheless, high-level surgical autonomy remains at an early stage, and its clinical deployment is constrained by data scarcity, limited generalizability, insufficient standardized benchmarks, safety-critical control requirements, interpretability, regulatory uncertainty, and ethical concerns regarding responsibility and supervision. Future progress will depend not only on algorithmic innovation, but also on rigorous validation, transparent human–robot collaboration frameworks, and evidence-based assessment of clinical value.
Jin-Fang Wang, Xinkang Zhang, Xinrong Chen· Journal of Mechanics in Medi...· 0 citations
Bipedal robots have gained a lot of attention in robotics because of their versatility in numerous application areas such as in rescue missions, military tasks, therapy, personal assistance and care for the aged. Their ability to move in tough and mixed-up spaces provides essential advantages over wheeled robots. However, they present challenges, such as complicated movement, balancing and control issues, and energy management problems. This paper utilized PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) to examine peer-reviewed publications in bipedal robots, covering both their modeling dynamics and strategic control, application areas and the challenges in development and management. After reviewing 165 of 15,214 publications that satisfied the inclusion criteria, the assessments were developed around the history of bipedal robots, marking important developments like dynamic control, passive-dynamic walking, and using sensors in real-time. Developments in artificial intelligence, machine learning, and reinforcement learning have made bipedal robots more stable, adaptable, and efficient. The novelty of this review lies in integrating rigid-body and reduced-order dynamic models, classical and learning-based control strategies, hardware limitations, and future research priorities within a unified comparative framework for bipedal robotics.
B. Kommey, E. Tamakloe, Safianu Umar et al.· AVITEC· 0 citations
This survey positions humanoid control at a turning point, converging toward a unified paradigm of physics-guided generative intelligence that integrates optimization, learning, and predictive reasoning.
Yan Gu, Guanya Shi, Fan Shi et al.· Science Robotics· 7 citations
Robotic systems are entering clinical care, but full autonomy remains constrained by patient variability, safety requirements, dynamic clinical environments, and the need for professional oversight. Human–robot collaboration (HRC) offers a more realistic path: clinicians retain judgment and responsibility, while robots and AI support sensing, planning, action, and decision‐making within defined task boundaries. In this narrative review, we examine recent advances in clinical HRC from a systems perspective, covering system architecture, learning and control, safety mechanisms, autonomy assessment, and real‐world deployment. We propose a dual‐brain framework, comprising a professional brain for clinical reasoning and decision support, and a physical brain for embodied sensing, planning, and task execution. Using examples from imaging, rehabilitation, surgery, and outpatient care, we argue that clinical autonomy is developing in stages from clinician‐supervised imaging and decision support to bounded robotic subtasks and workflow‐level coordination, instead of through unsupervised replacement of healthcare professionals. We further discuss how robustness, trust, accountability, and human factors shape safe adoption in practice. By framing autonomy as collaborative rather than substitutive, this review provides a unifying foundation for designing and evaluating the next generation of autonomous clinical systems.
Conventional microsurgery faces the major challenges of "tremor, risk, and fatigue" due to its demanding precision requirements, the fragility of tissues, restricted operating space, and the physiological limits of the human body. Microsurgical robots, equipped with key technologies such as tremor suppression, motion scaling, multimodal sensing fusion, and teleoperation, can significantly enhance surgical precision and stability while reducing surgeon fatigue and the risk of postoperative complications. This paper provides a systematic review of the current research progress and technical achievements of master-slave microsurgical robots with the greatest development potential, and offers an in-depth analysis of the challenges and future directions of their key technologies. Finally, it outlines the trend that microsurgical robots are evolving toward greater versatility, broader perceptual and interactive capabilities, higher autonomy, and deeper system specialization. This paper aims to provide readers with a more precise understanding of the current state and future trajectory of microsurgical robot research, and to promote its accelerated clinical adoption through broader and more in-depth investigation.
Unknown authors· Measurement science and tech...· 0 citations
Human-Robot Collaboration (HRC) plays a vital role in dynamic, high mix, low volume industrial scenarios such as remanufacturing, which frequently face workcell rearrangements. Traditional setups are constrained by power and data cabling, restricting modularity and reconfigurations, while the selection of commercial wireless devices suitable for real-time perception and safe collaboration are limited in availability. This paper presents a highly flexible, wireless, 5G-based system that serves as a versatile experimental testbed for applications including remanufacturing, operator training, and user studies. To eliminate infrastructure barriers, the workcell integrates a novel battery-powered, multi-sensor platform prototype. Additionally, to support operator safety and system adaptability across environmental shifts, the system integrates a computer vision module for object detection and pose estimation, further augmented for robust hand recognition. Trained on synthetic and real data, the model reliably detects oriented grasping poses and human hands across varying lighting and background conditions (with an mAP@50-95 of 97.74 +- 0.10% and a mean inference time of 12.5 ms). Offloading these computationally intensive tasks to the edge via 5G, the proposed architecture contributes to resolving the bandwidth-latency trade-off. To demonstrate portability, the system was implemented in both Hungary and Norway, and was evaluated across a combination of public and private, Standalone and Non-Standalone 5G infrastructures. The performed network experiments produced results in round-trip response times down to 12 ms in case of compatible network-device pairings, suitable for safe, adaptive HRC. However, these measurements also revealed practical limitations related to interoperability in current 5G deployments that should be addressed in future works.
E. Takacs, Mátyás Hajós, Á. Juniki et al.· 0 citations
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