From Lab to Internship and Back Again: Learning Autonomous Systems through Creating a Research and Development Ecosystem

Authors

  • Trevor Bihl Air Force Research Laboratory
  • Todd Jenkins Air Force Research Laboratory
  • Chadwick Cox KeyW Corporation
  • Ashley DeMange Air Force Research Laboratory
  • Kerry Hill Air Force Research Laboratory
  • Edmund Zelnio Air Force Research Laboratory

DOI:

https://doi.org/10.1609/aaai.v33i01.33019635

Abstract

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.

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Published

2019-07-17

How to Cite

Bihl, T., Jenkins, T., Cox, C., DeMange, A., Hill, K., & Zelnio, E. (2019). From Lab to Internship and Back Again: Learning Autonomous Systems through Creating a Research and Development Ecosystem. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 9635-9643. https://doi.org/10.1609/aaai.v33i01.33019635

Issue

Section

EAAI Symposium: Full Papers