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These days, we see different types of telepresence robots, and there has been a tremendous amount of research and development on these robots. Some telepresence robots have monitors to show the faces of remote operators and mobility to move around, but some do not have a monitor and instead have a robot face, and also they have robotic arms to do motions and perform functions. Many people feel that artificial intelligence (AI) in robots depends on their appearance, which is anthropomorphism, and telepresence robots without arm or body motions can also have a human presence. It is important to identify and configure how these robots give people a sense of presence and anthropomorphism by including human-like and robot-like faces and arm motions. We carried out web-based experiments and used videos of a telepresence robot ( $2\times 2$ between-participant study; face factor: human face, robot face; motion factor: moving, static) to investigate which factors significantly give users the sense of human presence and anthropomorphism in a robot. Our results show that participants felt that the robot had more anthropomorphism when its face was replaced with a human’s face and it did not make any motion. In addition, the robot’s motion invoked a feeling of human presence regardless of whether the face was human-like or robot-like. Our novel findings provide a guide for designing telepresence robots, revealing that motion enhances presence, and displaying the operator’s face increases anthropomorphism. These innovative insights offer a new approach to optimizing the design of telepresence robots depending on the desired user experience. When designing these robots, focusing on the sense of presence involves considering both stationary and moving robots to evoke a feeling of human presence. Conversely, to emphasize anthropomorphism, it is crucial to display the remote operator’s face.

The social acceptance of AI agents, including intelligent virtual agents and physical robots, is becoming more important for the integration of AI into human society. Although the agents used in human society share various tasks with humans, their cooperation may frequently reduce the task performance. One way to improve the relationship between humans and AI agents is to have humans empathize with the agents. By empathizing, humans feel positively and kindly toward agents, which makes it easier to accept them. In this study, we focus on tasks in which humans and agents have various interactions together, and we investigate the properties of agents that significantly influence human empathy toward the agents. To investigate the effects of task content, difficulty, task completion, and an agent’s expression on human empathy, two experiments were conducted. The results of the two experiments showed that human empathy toward the agent was difficult to maintain with only task factors, and that the agent’s expression was able to maintain human empathy. In addition, a higher task difficulty reduced the decrease in human empathy, regardless of task content. These results demonstrate that an AI agent’s properties play an important role in helping humans accept them.