The Robotic Decision Making Laboratory performs fundamental research in the growing area of robotic systems. We design planning, coordination, and learning techniques to improve robotic sensing and manipulation in the physical world. We work to develop scalable systems grounded in principled theoretical analysis capable of acting in changing, unstructured environments with imperfect information. A few of our recent research threads are listed below. Additional detail is provided on the research page.

  • Machine learning for inspection and classification with autonomous underwater vehicles
  • Communication-aware motion planning for robotic sensor networks
  • Decision making for personal robotic assistants
  • Multi-robot coordination for search and pursuit-evasion in the physical world
  • Tracking using non-line-of-sight ranging sensors