We are developing robots with socially appropriate spatial skills not only to travel around or near people, but also to accompany people side-by-side. As a step toward this goal, we are investigating the social perceptions of a robot's movement as it follows behind a person. This paper discusses our laser-based person-tracking method and two different approaches to person-following: direction-following and path-following. While both algorithms have similar characteristics in terms of tracking performance and following distances, participants in a pilot study rated the direction-following behavior as significantly more human-like and natural than the path-following behavior. We argue that the path-following method may still be more appropriate in some situations, and we propose that the ideal person-following behavior may be a hybrid approach, with the robot automatically selecting which method to use.

This paper reports on the results of a long-term experiment in which a social robot's facial expressions were changed to reflect different moods. While the facial changes in each condition were not extremely different, they still altered how people interacted with the robot. On days when many visitors were present, average interactions with the robot were longer when the robot displayed either a "happy" or a "sad" expression instead of a neutral face, but the opposite was true for low-visitor days. The implications of these findings for human-robot social interaction are discussed.

This paper presents results toward our ongoing research program into hands-off assistive human-robot interaction [6]. Our work has focused on applications of socially assistive robotics in health care and education, where human supervision can be significantly augmented and complemented by intelligent machines. In this paper, we focus on the role of embodiment, empirically addressing the question: "In what ways can the robot's physical embodiment be used effectively to positively influence human task-related behavior?" We hypothesized that users' personalities would correlate with their preferences of robot behavior expression. To test this hypothesis, we implemented an autonomous mobile robot aimed at the role of a monitoring and encouragement system for stroke patient rehabilitation. We performed a pilot study that indicates that the presence and behavior of the robot can influence how well people comply with their physical therapy.

Humans use expressions of emotion in a very social manner, to convey messages such as "I'm happy to see you" or "I want to be comforted," and people's long-term relationships depend heavily on shared emotional experiences. We believe that for robots to interact naturally with humans in social situations they should also be able to express emotions in both short-term and long-term relationships. To this end, we have developed an affective model for social robots. This generative model attempts to create natural, human-like affect and includes distinctions between immediate emotional responses, the overall mood of the robot, and long-term attitudes toward each visitor to the robot. This paper presents the general affect model as well as particular details of our implementation of the model on one robot, the Roboceptionist

Valerie the roboceptionist is the most recent addition to Carnegie Mellon's social robots project. A permanent installation in the entranceway to Newell-Simon hall, the robot combines useful functionality - giving directions, looking up weather forecasts, etc. - with an interesting and compelling character. We are using Valerie to investigate human-robot social interaction, especially long-term human-robot "relationships". Over a nine-month period, we have found that many visitors continue to interact with the robot on a daily basis, but that few of the individual interactions last for more than 30 seconds. Our analysis of the data has indicated several design decisions that should facilitate more natural human-robot interactions.