Adaptable Pouring: Teaching Robots Not to Spill using Fast but Approximate Fluid Simulation

Tatiana López Guevara, Nicholas Kenelm Taylor, Michael Gutmann, Subramanian Ramamoorthy, Kartic Subr

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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.
Original languageEnglish
Number of pages10
Publication statusPublished - 13 Nov 2017
Event1st Conference on Robot Learning 2017 - Mountain View, United States
Duration: 13 Nov 201715 Nov 2017
http://www.robot-learning.org/

Conference

Conference1st Conference on Robot Learning 2017
Abbreviated titleCoRL 2017
CountryUnited States
CityMountain View
Period13/11/1715/11/17
Internet address

Keywords

  • Fluid simulation
  • Pouring

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    López Guevara, T., Taylor, N. K., Gutmann, M., Ramamoorthy, S., & Subr, K. (2017). Adaptable Pouring: Teaching Robots Not to Spill using Fast but Approximate Fluid Simulation. Paper presented at 1st Conference on Robot Learning 2017, Mountain View, United States.