## 5 Case Study  
The case study aims to evaluate the RHA's ability to engage in natural language communication with users, utilize contextual information for proper task execution, and execute either individual actions or a series of actions according to a predefined task specification. In this proof-of-concept study, we tested the complete pipeline that connects healthcare-related knowledge to robot actions, enabling real-time interaction between the RHA and ChatGPT. The iterative nature of the development required more than 20 rounds of improvements, involving adjustments to task specifications, interaction guidelines, and repeated testing of task execution in the Gazebo simulation. Despite the complexity of these iterative improvements, once finalized, the responses from ChatGPT4 remained consistent, demonstrating the integration of structured and unstructured information into the robot's decision-making process. The environment used for this case study is a patient room simulated in Gazebo, with a size of 8 m \(\times\) 10 m, which includes both the patient room and an adjoining corridor. The simulation setup incorporates objects such as a patient, a bed, a TV, a water cup on a table, and predetermined locations for interaction, including the patient monitoring location, the patient-robot interaction point, and the robot waiting area. Detailed descriptions of the patient room objects are provided in Appendix A, \"Patient Room Description\". The robot platform used for this study is the Tiago Dual, which is equipped with an omnidirectional base to facilitate smooth navigation within the confined patient room space (in contrast to a differential base, which requires more space for turning). The Tiago Dual also has two manipulator arms with grippers and is equipped with a 2D laser scanner that supports localization and navigation. The code for RHA control, communication with ChatGPT, and terminal-based user interface were developed in ROS Noetic on Ubuntu 20.04 operating system. As shown in Figure 2(a), the Gazebo simulation initializes the patient room environment and spawns the RHA at a predetermined location. Figure 2(b) illustrates the RHA's internal representation of the environment and data streams, which utilizes the ROS AMCL libraries [51] for localization and navigation and MoveIt! libraries [52] for arm manipulation. The essential inputs for AMCL include a 2D map of the room and the robot's initial pose. The 2D map was generated using the PGM map creator [53], taken directly from the Gazebo simulation of the patient room without the robot spawned. To ensure that the robot's internal work representation aligns accurately with the Gazebo simulation environment, the map generated from the PGM creator was adjusted in both location and orientation to match the Gazebo simulation's coordinate system. This step is crucial for ensuring that the robot can accurately interact with its environment. A similar effort would be required when implementing this system with a real robot in a patient room. This study does not include sensor-based perception of the environment utilizing object detection and localization. Instead, all relevant inputs are provided through prompts and contextual information provided (information in appendices) and by directly accessing model data within the Gazebo simulation. This approach enables the case study to focus on evaluating the robot's ability to adaptively plan its operations without relying on hardcoded logic, relying solely on its interaction with ChatGPT-4. After the RHA spawns successfully in Gazebo and its internal information is visualized in RVIZ, a ROS node is executed to manage interaction with ChatGPT and users. The system first initializes the ChatGPT server and starts communication. The RHA submits the first prompt: \"I am Robotic Health Attendant (RHA) for patient care. I have several functions to help patients. I am working with clinicians like nurses and doctors as a team member.