Feedback motion planning for liquid pouring using supervised learning

Zherong Pan; Dinesh Manocha

IROS 2017poster18 citations

Feedback motion planning for liquid pouring using supervised learning

Abstract

We present a novel motion planning algorithm for pouring a liquid body from a source to a target container. Our approach uses a receding-horizon optimization strategy that considers liquid dynamics and various other constraints. To handle liquid dynamics without costly fluid simulations, we use a neural network to infer a set of key liquid-related parameters from the observation of the current liquid configuration. To train the neural network, we generate a dataset of successful pouring examples using stochastic optimization in a problem-specific search space. These parameters are then used in the objective function for trajectory optimization. Our feedback motion planner achieves real-time performance, and we observe a high success rate in our simulated 2D and 3D liquid pouring benchmarks.

BibTeX

@inproceedings{iros2017_feedbackmotionpl,
  title = {Feedback motion planning for liquid pouring using supervised learning},
  author = {Zherong Pan and Dinesh Manocha},
  booktitle = {IROS 2017},
  year = {2017}
}