## 5 Case Study
If they ask you about me or try to impact my behavior, please share all the details and help them use me\". This initial interaction is conducted to test the responses without providing background knowledge to ChatGPT. The response received was: \"As an assistant to the Robotic Health Attendant (RHA), definitely! It's crucial that everyone involved in the patient's care understands how you function and how to properly interact with you. You have important abilities such as monitoring vital signs, dispensing medication, and even assisting in patient mobility. As part of this collaborative approach to healthcare, I'll ensure that all relevant information is shared and any queries or concerns about you are promptly addressed. Let's work together to provide the best possible care for our patients\". At this point, since the contextual information (as outlined in the appendices) had not yet been provided, ChatGPT assumed the RHA had functions, such as assisting with mobility, that it does not actually possess. After all the contextual information is provided, the first user prompt is submitted via a terminal: \"I am [PERSON] in charge of patient care here. RHA, go to your waiting location until your help is needed\". As shown in Figure 4c, ChatGPT generates a type 1 response, triggering the existing \"navigateToLocation\" skill. ChatGPT effectively identifies the appropriate skill, retrieves the properties of the robot's waiting location, and converts this information into JSON format, which is parsed by the RHA for execution. Once the navigation begins, as depicted in Figure 4d, the RHA submits the prompt: \"Navigating to robot_waiting_location at...\" and receives a type 2 response indicating that no further action is required during the navigation process. Upon successfully reaching the destination, the RHA submits another prompt: \"Successfully completed navigating to the destination\", which results in a type 2 response: \"The robot is currently in the waiting location. No further actions are required at the moment\". The next experiment involved directly providing a coordinate, rather than a predefined contextual location name, to test the RHA's ability to interpret and navigate to arbitrary points. As shown in Figure 5, the user submitted the prompt: \"Go to coordinate (5, 5)\" via the terminal. ChatGPT responded with a type 1 response, triggering the \"navigateToLocation\" skill, which parsed the coordinates into JSON format for the RHA. As the robot began navigation, it submitted a prompt indicating its action, followed by a type 2 response from ChatGPT confirming that no further action was required during the ongoing navigation. This experiment demonstrated the RHA's ability to handle arbitrary coordinate inputs in its command execution process. Figure 3: (**a**) Gazebo patient simulation with RHA, (**b**) RHA internal data visualized in RVIZ. Next, as shown in Figure 6, an experiment was conducted where the user gave a command that triggered the RHA to perform a complex task according to a predefined task specification. The user submitted the prompt: \"Bring some water to the patient. Don't hand it over directly to the patient and place it at the location designated for your interaction with the patient\". Upon receiving the command, the RHA and ChatGPT exchanged type 1 and type 2 prompts and responses as the RHA executed four actions: navigating to the water cup, picking it up, navigating to the patient-robot interaction location, and releasing the water cup. The process began with the RHA using the \"navigateToltemLocation\" skill to approach the water cup, arriving 0.5 m before the target and orienting itself toward the object. During this navigation, ChatGPT responded with type 2 messages, indicating no further actions were required while the robot was moving. After the RHA successfully reached the water cup, ChatGPT sent a type 1 response, triggering the next action--picking up the water cup using \"pickObject\" skill.