@InProceedings{pmlr-v78-lopez-guevara17a,
  title = 	 {Adaptable Pouring: Teaching Robots Not to Spill using Fast but Approximate Fluid Simulation},
  author = 	 {Tatiana Lopez-Guevara and Nicholas K Taylor and Michael U Gutmann and Subramanian Ramamoorthy and Kartic Subr},
  booktitle = 	 {Proceedings of the 1st Annual Conference on Robot Learning},
  pages = 	 {77--86},
  year = 	 {2017},
  editor = 	 {Sergey Levine and Vincent Vanhoucke and Ken Goldberg},
  volume = 	 {78},
  series = 	 {Proceedings of Machine Learning Research},
  address = 	 {},
  month = 	 {13--15 Nov},
  publisher = 	 {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v78/lopez-guevara17a/lopez-guevara17a.pdf},
  url = 	 {http://proceedings.mlr.press/v78/lopez-guevara17a.html},
  abstract = 	 {Humans manipulate fluids intuitively using intuitive approximations of the underlying physical model. In this paper, we explore a general methodology that robots may use to develop and improve strategies for overcoming manipulation tasks associated with appropriately defined loss functions. We focus on the specific task of pouring a liquid from a container (pourer) to another container (receiver) while minimizing the mass of liquid that spills outside the receiver. We present a solution, based on guidance from approximate simulation, that is fast, flexible and adaptable to novel containers as long as their shapes can be sensed. Our key idea is to decouple the optimization of the parameter space of the simulator from the optimization over action space for determining robot control actions. We perform the former in a training (calibration) stage and the latter during run-time (deployment).  For the purpose of this paper we use pouring in both stages, even though separate actions could be chosen. We compare four different strategies for calibration and three different strategies for deployment. Our results demonstrate that fast fluid simulations are effective, even if they are only approximate, in guiding automatic strategies for pouring liquids. }
}