This paper describes results from a large-scale, complex human study using non-facial and non-verbal affect for victim management in robot-assisted Urban Search and Rescue Applications. Statistically significant results are presented that indicate participants felt emotive robots were more calming, friendlier, and attentive.

Fifteen scientists from six universities and five companies were embedded with a team of search and rescue professionals from the Federal Emergency Management Agency's Indiana Task Force 1 in August 2003 at a demolished building in Lebanon, Indiana. The highly realistic 27-hour exercise enabled participants to identify the prevailing issues in rescue robotics. Perception and situation awareness were deemed the most pressing problems, with a recommendation to focus on human-computer cooperative algorithms because recognition in dense rubble appears far beyond the capabilities of computer vision for the near term. Human-robot interaction was cited as another critical area as well as the general problem of how the robot can maintain communications with the rescuers. The field exercise was part of an ongoing grant from the National Science Foundation to the Center for Robot-Assisted Search and Rescue CRASAR), and CRASAR is sponsoring similar activities in summer 2004.

The three competitions -- (1) AAAI Mobile Robot, (2) AUVS Unmanned Ground Robotics, and (3) IJCAI RoboCup -- were used in different years for an introductory undergraduate robotics course, an advanced graduate robotics course, and an undergraduate practicum course. Based on these experiences, a strategy is presented for incorporating competitions into courses in such a way as to foster intellectual maturation as well as learn lessons in organizing courses and fielding teams. The article also provides a classification of the major robot competitions and discusses the relative merits of each for educational projects, including the expected course level of computer science students, equipment needed, and costs.

This article discusses five years of experience using three international mobile robot competitions as the foundation for educational projects in undergraduate and graduate computer science courses. The three competitions -- (1) AAAI Mobile Robot, (2) AUVS Unmanned Ground Robotics, and (3) IJCAI RoboCup -- were used in different years for an introductory undergraduate robotics course, an advanced graduate robotics course, and an undergraduate practicum course. Based on these experiences, a strategy is presented for incorporating competitions into courses in such a way as to foster intellectual maturation as well as learn lessons in organizing courses and fielding teams. The article also provides a classification of the major robot competitions and discusses the relative merits of each for educational projects, including the expected course level of computer science students, equipment needed, and costs.

This article posits the idea of robot learning as a new subfield. The results of the Robolearn-96 Workshop provide evidence that learning in modern robotics is distinct from traditional machine learning. The article examines the role of robotics in the social and natural sciences and the potential impact of learning on robotics, generating both a continuum of research issues and a description of the divergent terminology, target domains, and standards of proof associated with robot learning. The article argues that although robot learning is a new subfield, there is significant potential for synergy with traditional machine learning if the differences in research cultures can be overcome.