Broadly, my research focuses on the control of stochastically interacting things. My goal is to steer global system behavior in the face of random, but tunable, behavior of individual sub components. How do natural systems achieve high reliability while being built from unreliable, stochastic sub components? What is the right level of abstraction for modeling such systems, especially when the number of sub components is large?
I've worked mostly with robotic test-beds where randomness is introduced by choice to give robustness, scalability, or simply to explore control strategies. However, many of the ideas come from my interactions with the synthetic biologists in our research group. Currently, I'm thinking about control strategies for a particular system model, Stochastic Reaction Networks.
The Factory Floor Testbed is an experiment to explore scalable, robust, multi robot construction hardware and algorithms. It consists of modular robots that can build arbitrary lattice structures from two types of raw materials. The hardware is built by the (ModLab) at the University of Pennsylvania. My role in the project is the design of models and robust construction algorithms for the testbed.
- N. Napp and E. Klavins, Load Balancing for Multi-Robot Construction, International Conference on Robotics and Automation (ICRA11), Shanghai, China, 2011. (accepted, in revision)
- N. Napp and E. Klavins. A Compositional Framework for Programming Stochastically Interacting Robots. International Journal of Robotics Research (in review).
- N. Napp and E. Klavins. Robust by Composition: Programs for Multi-Robot Systems. International Conference on Robotics and Automation (ICRA10), Anchorage, AK, USA, pp 2459-66, 2010. pdf
- N. Napp, S. Burden, and E. Klavins. Setpoint Regulation for Stochastically Interacting Robots. Autonomous Robots, special RSS Issue, Volume 30(1) 2011, pp 57-71.
- N. Napp, S. Burden, and E. Klavins. Setpoint Regulation for Stochastically Interacting Robots. Proceedings of Robotics: Science and Systems (RSS09), Seattle, Washington, USA, June 2009. pdf
- N. Napp, D. Thorsley, and E. Klavins. Hidden Markov Models for Non-Well-Mixed Reaction Networks. Proceedings of American Control Conference (ACC09), St. Louis, Missouri, USA, June 2009. pdf
- N. Napp and E. Klavins. An extended state-space markov chain model for self-organizing systems in non-well-mixed environments. In 4th Annual Conference on the Foundations of Nanoscience, Snowbird, UT, April 2007. Contributed Talk + Abstract.
- S. Burden, N. Napp, and E. Klavins. The statistical dynamics of programmed robotic self-assembly. In International Conference on Robotics and Automation (ICRA06), pp 1469-76, Orlando, FL, USA, 2006. pdf
- S. Burden, N. Napp, and E. Klavins. Tuning reaction networks for self-assembly. In 3rd Annual Conference on the Foundations of Nanoscience, Snowbird, UT, April 2006. Poster + Abstract.
- E. Klavins, S. Burden, and N. Napp. Optimal rules for programmed stochastic self-assembly. Proceedings of Robotics: Science and Systems (RSS06), Philadelphia, PA, 2006. pdf
- J. Bishop, S. Burden, E. Klavins, R. Kreisberg, W. Malone, N. Napp, and T. Nguyen. Self-organizing programmable parts. In International Conference on Intelligent Robots and Systems (IROS05), Edmonton, AB, Canada, 2005. pdf
B.S., Engineering, Math, Harvey Mudd College CA, 2003
M.S., Electrical Engineering, University of Washington WA, 2006PhD., Electrical Engineering, University of Washington WA, 2011