## 1 Introduction  
Healthcare workers, such as nurses, physicians, lab technicians, and support staff, are under significant physical and mental strain, exacerbated by chronic staff shortages and demanding working conditions [1]. They encounter numerous risks, including high stress and burnout from long and physically demanding work hours, exposure to infectious diseases, and the need to handle hazardous substances like antineoplastic agents [2]. A survey involving 42,000 healthcare workers, including physicians, nurses, clinical staff, and non-clinical staff, revealed that about 50% of healthcare workers are experiencing burnout, with 31.1% to 41% of them considering leaving the profession due to high stress and work overload [3]. Additionally, data from the American Hospital Association (AHA) indicate a critical trend in workforce stability, noting that the vacancy rate for nursing personnel surged by up to 30% between 2019 and 2020 [4]. This trend is expected to continue, with projections showing a potential shortage of up to 3.2 million healthcare workers by 2026 [4]. The severe strain on healthcare workers coupled with staffing shortages directly impacts patient care, leading to potential delays in treatment, reduced time for patient interaction, and increased risk of errors in medical care. In response, recent advancements in automationand robotics offer promising solutions to alleviate these pressures and enhance healthcare quality by supporting the overburdened healthcare workforce. Just as robotization is revolutionizing many industries, the integration of service robots is significantly transforming the healthcare sector. According to the McKinsey Global Institute's 2017 report, artificial intelligence (AI) and robotics technologies available at that time could automate 36% of tasks in the US healthcare sector, particularly those related to physical work, significantly reducing operational costs [5]. Medical service robots are now performing a variety of repetitive and labor-intensive tasks that are traditionally carried out by humans. Robotized tasks include lifting and transporting patients, dispensing medications, and handling supplies, as well as performing cleaning and disinfection duties. Noteworthy developments include nursing assistant robots that lift and move bedridden patients [6; 7; 8; 9]; robotic wheelchairs with autonomous navigation for patient transport [10; 11; 12]; delivery robots distributing materials, supplies, lab samples, and medications [13; 14]; cleaning and sterilization robots [15; 16]; and robots providing physical and emotional care services in patient rooms and homes [17; 18]. The adoption of medical service robots like these can alleviate the physical and mental burdens on healthcare workers, streamline operations, enhance service efficiency, and enable a stronger focus on patient-centered care, ultimately elevating the overall quality of care [19]. Despite the advancements in the field, the practical and broader adoption of service robots in medical environments remains limited by significant challenges. Current medical service robots often lack the cognitive abilities to autonomously manage \"unscripted\" scenarios frequently encountered in healthcare settings. These robots typically cannot independently recognize the evolving needs of patients or clinicians, or autonomously plan and execute necessary actions without human intervention. Existing robots primarily rely on pre-programmed instructions and standardized protocols, performing optimally in controlled environments. For instance, healthcare robots like Hobbit [17] and Lio [18] operate using fixed communication protocols that involve graphical user interfaces (GUIs), speech recognition, and gesture recognition systems. Similarly, the operations of automatic robotic wheelchairs [10; 11; 12] and delivery robots [13; 14] are controlled by inputs from their GUIs or via predefined voice commands and button activations from users.