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High-stress environments, such as a NASA Control Room, require optimal task performance, as a single mistake may cause monetary loss or the loss of human life. Robots can partner with humans in a collaborative or supervisory paradigm. Such teaming paradigms require the robot to appropriately interact with the human without decreasing either»s task performance. Workload is directly correlated with task performance; thus, a robot may use a human»s workload state to modify its interactions with the human. A diagnostic workload assessment algorithm that accurately estimates workload using results from two evaluations, one peer-based and one supervisory-based, is presented. 

The Robotics Program at Oregon State University has been running an NSF-funded summer Research Experiences for Undergraduates (REU) site since 2014. Over twenty students per year (on average) have participated in the site, spending ten weeks embedded in the OSU Robotics Program. Our main focus with this REU Site is to give the participants a com- plete research experience, from problem definition to the fi- nal presentation of results, “in miniature”. Our secondary ed- ucational objectives are: 1) Teach basic non-technical skills needed for graduate work, such as time management and lit- erature review, 2) Provide details on how to apply to gradu- ate school and for funding, 3) Clarify what we look for in a graduate student, and 4) Detail what to expect from the grad- uate student experience. In this paper, we describe the over- all structure of the participants’ summer experience, outline some of the training materials that we use, describe the moti- vations for our approach, and discuss the lessons that we have learned after running the program for a number of years.