Position Estimation, Planning, and Learning with Partially Observable Markov Models
I am particularly interested in making Xavier and Amelia navigate
autonomously and robustly in corridor environments. This includes work
on position estimation, planning, plan monitoring, and learning. My
work shows that one can build a whole robot architecture around
Partially Observable Markov Decision Process (POMDP) models. POMDP
models allow the robots to account for actuator and sensor uncertainty
and to integrate topological map information with approximate metric
information. They also allow the robots to act and learn even if they
are uncertain about their current location.
Check out my home page
and my other projects!
Last update: January 1 1996