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.

A neural network-based pilot-induced oscillations (PIO) detection algorithm and a PIO compensation method were shown to be highly effective during a series of piloted simulations. During PIO, the aircraft oscillates in a manner that is out of phase with the pilot's control inputs. The pilot is not aware when PIO are driven by pilot-aircraft coupling, so he may continue driving the oscillations in an ineffective attempt to stop them. PIO have caused a significant number of aircraft accidents.