Robotic Cleaning through Dirt Rearrangement Planning with Learned Transition Models

Elliott, Sarah; Cakmak, Maya

Robotic Cleaning through Dirt Rearrangement Planning with Learned Transition Models

URL

S. Elliott and M. Cakmak, “Robotic Cleaning through Dirt Rearrangement Planning with Learned Transition Models,” 2018, doi: 10.1109/ICRA.2018.8460915.

Abstract

We address the problem of enabling a 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 actions based on a clustering of dirt states and heuristics for planning. We demonstrate the effectiveness of our approach on challenging cleaning tasks through implementations on PR2 and Fetch robots.

BibTeX Entry

@inproceedings{elliott2018icra,
  title = {Robotic Cleaning through Dirt Rearrangement Planning with Learned Transition Models},
  author = {Elliott, Sarah and Cakmak, Maya},
  year = {2018},
  booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
  type = {conference},
  doi = {10.1109/ICRA.2018.8460915}
}