@article{Bihl_Jenkins_Cox_DeMange_Hill_Zelnio_2019, title={From Lab to Internship and Back Again: Learning Autonomous Systems through Creating a Research and Development Ecosystem}, volume={33}, url={https://ojs.aaai.org/index.php/AAAI/article/view/5027}, DOI={10.1609/aaai.v33i01.33019635}, abstractNote={<p>As research and development (R&D) in autonomous systems progresses further, more interdisciplinary knowledge is needed from domains as diverse as artificial intelligence (AI), bi-ology, psychology, modeling and simulation (M&S), and robotics. Such R&D efforts are necessarily interdisciplinary in nature and require technical as well as further soft skills of teamwork, communication and integration. In this paper, we introduce a 14 week, summer long internship for developing these skills in undergraduate science and engineering interns through R&D. The internship was designed to be modular and divided into three parts: training, innovation, and application/integration. The end result of the internship was 1) the development of an M&S ecosystem for autonomy concepts, 2) development and robotics testing of reasoning methods through both Bayesian methods and cognitive models of the basal ganglia, and 3) a process for future internships within the modular construct. Through collaboration with full-time professional staff, who actively learned with the interns, this internship incorporates a feedback loop to educate and per-form fundamental R&D. Future iterations of this internship can leverage the M&S ecosystem and adapt the modular internship framework to focus on different innovations, learning paradigms, and/or applications.</p>}, number={01}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Bihl, Trevor and Jenkins, Todd and Cox, Chadwick and DeMange, Ashley and Hill, Kerry and Zelnio, Edmund}, year={2019}, month={Jul.}, pages={9635-9643} }