Learning responsive robot behavior by imitation
In this paper we present a new approach for learning responsive robot behavior by imitation of human interaction partners. Extending previous work on robot imitation learning, that has so far mostly concentrated on learning from demonstrations by a single actor, we simultaneously record the movements of two humans engaged in on-going interaction tasks and learn compact models of the interaction. Extracted interaction models can thereafter be used by a robot to engage in a similar interaction with a human partner. We present two algorithms for deriving interaction models from motion capture data as well as experimental results on a humanoid robot.
By Tony Martini, Economics ::: Advisor: Michael Jones ::: Presentation ID: AM_D39 ::: Abstract: This study suggests new methods of measuring the effectiveness of universities in protecting students from the impact of automation on the labor market. Using US government data on occupational activities and traditional measures of university outcomes, we produce a ranking of college majors by their susceptibility to automation and determine correlations between predicted traditional and automation-based outcomes. These are combined into a complete metric for the "automation resistance" of a university. Using an approach centered on distinctly human skills, occupational activities can be ranked by their automation resistance. Institutions which develop student skills with a focus on automation resistance should likewise demonstrate greater long-term viability. We demonstrate this methodology through a case study using data from the University of Cincinnati.