This proposal outlines a research and educational plan to advance decision-making techniques for robots that cooperate with human operators. Because humans far exceed the abilities of state-of-the-art robots in vision, creativity, and adaptability, interest is rapidly growing in a human-centered approach to robotics: combining the strengths of humans with the superior precision and repeatability of robots. And yet, our available motion planning tools, while powerful at computing motions for complex autonomous tasks, are poorly suited for human-centered applications that demand responsive and natural motions. This proposal hypothesizes that a new cooperative motion planning paradigm will support major advances in intuitiveness and task performance of human-operated robots such as intelligent vehicles, tele-surgery systems, search and-rescue robots, and household robots. This hypothesis is echoed in an educational plan that aims to train engineers with cross-disciplinary strengths that bridge both the technical and social dimensions of robotics. Initial human subjects studies on novice operators with the PI's cooperative motion planning algorithms suggest that the technique leads to dramatic reductions in task completion time and collision rate in cluttered environments. The proposed work will conduct further investigations along this line of research to 1) identify characteristics of cooperative planners - such as optimality, responsiveness, and completeness - that yield effective human-operator systems, both in terms of objective performance metrics and subjective preferences, 2) to design planners that optimize cooperativity metrics under computational resource and communication constraints, and 3) to enhance the capabilities of such planners to assist operators in complex manipulation tasks.
The planners developed in this research and the rich datasets acquired via user studies will serve as resources to help human-robot interaction (HRI) researchers design safe and socially acceptable robot behaviors. Moreover, advances in cooperative motion planning may have long-term social and economic impact by enabling new applications of robotics in driver assist systems, space exploration, medicine, household robotics, manufacturing, and construction. Research is integrated with education in a range of activities that include CS curriculum development, development of a new graduate course on optimization and machine learning, and in new software libraries for robotics education. New modules on motion planning, behavior recognition, and HRI will be incorporated in AI and robotics courses. An REU is requested for each summer of the grant and will be recruited from a minority-serving institution in cooperation with the Alliance for the Advancement of African-American Researchers in Computing (A4RC). One or more IU undergraduates will be involved in research and mentored according to the Undergraduate Research Opportunities in Computing (UROC) program, with preference given to minority and women students.
