# Robot Control Platform for Multimodal Interactions with Humans Based on ChatGPT
## 4 Experiments  
To verify the influence of the developed robot control platform on users, an experiment with users was designed. A group of users interacted with the Pepper robot. Their negative attitudes towards robots scale [23] and robot anxiety scale [24] scores were measured before and after interaction using appropriate questionnaires. Said questionnaires were translated to Polish to accommodate users who do not know English or do not feel comfortable using it. In both cases we made our own translation, which, in the case of NARS, is almost identical to [25]. In Figure 2, the interaction flow used in the experiments is presented. Initially, an experimenter welcomed the participant and seated them at the counter where the participant filled out the before questionnaires and was informed about the experiment procedure, following which, they were taken to a robot. Next, the participant was asked to sit in front of Pepper and wait a moment for the experiment to start. At the beginning, the purely technical part of the experiment was explained to the participant, i.e., how to handle robot interaction. After everything was prepared and the participant was ready, the experimenter left the robot and human alone, and the interaction began shortly thereafter. In the opening sequence, the Robot states its name, purpose and capabilities, i.e., what interactions are possible. Then the robot waits for user input, transforms it into text using the speech recognition module, and acts according to Figure 4 by executing recognized action on the robot using the pepper module or sending a message to ChatGPT. Next, ChatGPT answers or [PERSON]'s action execution ends, then the robot waits for the next input from the user. Conversation continues in this manner until the user says goodbye or it was ended by the experimenter in the right moment (when the user lost interest in the conversation or forgot how to end it by himself). At the end, the robot says the goodbye sequence, thanking the user for the conversation. The user can freely pick the topic of conversation, and as we are using ChatGPT's vast knowledge base, we deemed it unnecessary to limit or lead the user to a certain topic of conversation. After the experiment, the participants filled out the after questionnaires and had a short conversation about the experiment and robots with the experimenter. The tested population consisted of 20 people of both sexes with ages ranging from 18 to 60. The participants were mainly teachers and staff hired for a teachers conference, as we decided to perform an experiment alongside said conference. Unfortunately, due to the uncooperativeness of some of the participants (missing one of the questionnaires or  
Figure 4: Interaction flow used in experiments. questions) and unpredictable external interactions, we had to strike most of the participants from the final results. In the final data, we could use 10 participants. The results, which compare the NARS before and after the interaction, are presented in Figure 5. As we can see, our system works well and positively influences user perception of robots. After the conversation with [PERSON], people claimed to be more concerned about the robot being alive, and they were less paranoid talking with the robot. After conversation with the robot, users also more often predicted that robots will dominate the future. At the same time, the feelings of unease while given a job that requires cooperation with a robot decreased; as well, the perception of robots that have emotions and the possibility of making friends with them improved. The negative attitude towards the robot increased in cases of robots having a bad influence on children and users being too dependent on robots. The consideration for children might not be connected to robots but with ChatGPT capabilities and how they will influence the learning process of children. A neutral attitude towards the word \"robot\" suggests no bias towards robots. Fourteen questions can be divided into three groups.