Abstract: Privacy is important for everybody, and the advent of robotics technology poses new and unique privacy concerns that need to be addressed. This talk introduces "privacy-sensitive robotics," the growing study of privacy issues and how to address them for useful and acceptable human-robot interactions. Two studies will be presented. The first looks at interfaces for specifying what's private to a robot. Physical and persistent interfaces are tested alongside a traditional point-and-click setup in a live user study with a real PR2 robot and realistic office environment. The second study is about the effect of context on people's privacy concerns. Subjects responded to a video of a telepresence robot in their home; we measured the impact of wrapping this scenario in different interpretive frames. It will become clear that privacy-sensitive robotics is a largely untouched research area despite deep privacy literature in neighboring fields. Much more research is needed soon to match the pace of technological development.

Biography: Matthew Rueben is a Robotics PhD candidate in the Personal Robotics group at Oregon State University. He earned the H.B.S. Degree in Mechanical Engineering, also from Oregon State University, in 2013. Matt's current research centers on privacy concerns about mobile robots. This includes the factors that determine people's privacy concerns about robots, what objects and places people consider private, and how they might communicate these preferences to robots.

Abstract: Robotics is undergoing an exciting transition from factory automation to the deployment of autonomous systems in less structured environments, such as warehouses, hospitals and homes. One of the critical barriers to the wider adoption of autonomous robotic systems in the wild is the challenge of achieving reliable autonomy in complex and changing human environments. In this talk, I will discuss ways in which innovations in learning from demonstration and remote access technologies can be used to develop and deploy autonomous robotic systems alongside and in collaboration with human partners. I will present applications of this research paradigm to robot learning, object manipulation and semantic reasoning, as well as explore some exciting avenues for future research in this area.

Biography: Sonia Chernova is the Catherine M. and James E. Allchin Early-Career Assistant Professor in the School of Interactive Computing at Georgia Tech, where she directs the Robot Autonomy and Interactive Learning research lab. Her research spans semantic reasoning, human-robot interaction, interactive machine learning and cloud robotics, with the focus on developing robots that are able to effectively operate in human environments. She is the recipient of the NSF CAREER, ONR Young Investigator and NASA Early Career Faculty awards.