Peter Trautman

Research & Engineering

I am broadly interested in 1) autonomous robots operating in dynamic environments according to a variable global objective and 2) assistive technologies in home, automotive, clinical, factory, defense, and security settings. I approach these problems using the tools of probabilistic inference (to enable an autonomy that is flexible enough to support a wide variety of human inputs) and formal methods (to provide insight into how to correctly model these probabilistic distributions).


Dr. Trautman received his B.S. in Physics and Applied Mathematics from Baylor University in 2000. He then entered the United States Air Force, serving first as an analyst at the National Air and Space Intelligence Center, and then as a program manager/researcher at the Sensors Directorate (RYAT).

In 2005, he returned to graduate school at Caltech, completing his Ph.D. in Control and Dynamical Systems in 2012. His thesis research focused on robot navigation in dense human crowds, the result of which was a probabilistic model of human robot cooperation and a 6 month case study in Caltech’s student cafeteria.

From 2012 until early 2014, Dr. Trautman was a Senior Engineer at the Boeing company, where he developed navigation and localization technology for commercial aircraft assembly robots. Additionally, he served as the sensing team lead for Caltech’s DARPA Grand Challenge entry in 2006, has consulted for Toyon Research and Applied Minds Inc., and has conducted research in physics at the Los Alamos National Laboratory, the University of California at San Diego, and Worcester Polytechnic Institute. He was a Best Paper Finalist at ICRA 2013 for his work on autonomous crowd navigation.