## 7 Limitations and Future Studies  
While this study provides a foundational framework that connects key components, such as the knowledge base, human-language instructions, patient room information, and robot control software, it does so with limited use cases. Further efforts are necessary to enable the full inclusion of the RHA within clinical teams. A comprehensive study would need to document routine patient care tasks, identify specific burdens faced by clinicians, and design robot functions that address these burdens effectively. This would allow for a more integrated role for the RHA in real-world healthcare environments. To address this limitation, we plan to use the real robot in more realistic scenarios in future studies to test the RHA's performance under real-world conditions. One significant limitation of this study is its reliance on hardcoded patient room information for situational understanding. The RHA's comprehension of the patient room is based on pre-programmed data points, limiting its ability to autonomously adapt to dynamic environments. Future work should explore multimodal situational awareness, which would require a substantial amount of training data and advanced sensors to model the complexities of healthcare environments. In the next phase of our research, we will process sensor data directly using LLMs like LLMa to dynamically update the patient room information and derive the required robotic actions. Additionally, the current implementation relies on external APIs for LLM interaction, specifically the ChatGPT API, which can introduce latency and inefficiencies in time-sensitive healthcare applications. To mitigate these challenges, future research should focus on developing or fine-tuning open-source LLMs that can be deployed locally, thereby reducing response times while still ensuring high-level decision-making capabilities. Currently, the response time of ChatGPT was about 2 s, which is not acceptable for real-time interactions, particularly in close proximity to people. Moreover, the LLM in this study understands only the high-level structure of robot control software, lacking the capacity to modify or generate detailed code to execute robot functions. Future work could extend the role of the LLM, allowing it to generate robot functions in real-time based on the specific needs of patients, clinicians, or unexpected changes in the environment. Additionally, more sophisticated motion planning, especially in safe human-robot interaction and object handling, will be explored. Learning-based approaches for developing navigation and manipulator planning policies are needed for these applications. Lastly, the study does not yet address the challenges involved in real-time applications. Implementing this framework in real-world healthcare environments will require extensive testing to ensure that the robot's actions are executed within acceptable timeframes, especially in scenarios where delays could impact patient safety.  
**Author Contributions:** Conceptualization, K.K., M.C. and J.W.; methodology, K.K. and P.-C.H.; software, K.K., A.R. and R.C.; validation, K.K. writing--original draft preparation, K.K.; writing--review and editing, T.W.; supervision, J.W.; project administration, J.W.; funding acquisition, K.K., E.R. and J.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.