Title: Using Learned Transition Models to Rearrange Dirt on a Surface

Advisors: Maya Cakmak and Dieter Fox

Abstract: We address the problem of enabling a robot manipulator to move arbitrary amounts and configurations of dirt on a surface to a goal region using a cleaning tool. We represent this problem as heuristic search with a set of primitive dirt-oriented tool actions. We present dirt and action representations that allow efficient learning and prediction of future dirt states, given the current dirt state and applied action. We also present a method for sampling promising tool actions based on a clustering of dirt states and heuristics for planning. We demonstrate the effectiveness of our approach on challenging cleaning tasks through an implementation on the PR2 robot.

CSE 503
Tuesday, February 14, 2017 - 11:00 to 12:30