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arXiv · 1609.03433

Feedback Motion Planning for Liquid Transfer using Supervised Learning

Zherong Pan·Dinesh Manocha
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Abstract

We present a novel motion planning algorithm for transferring a liquid body from a source to a target container. Our approach uses a receding-horizon optimization strategy that takes into account fluid constraints and avoids collisions. In order to efficiently handle the high-dimensional configuration space of a liquid body, we use system identification to learn its dynamics characteristics using a neural network. We generate the training dataset using stochastic optimization in a transfer-problem-specific search space. The runtime feedback motion planner is used for real-time planning and we observe high success rate in our simulated 2D and 3D fluid transfer benchmarks.

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Zherong Pan, Dinesh Manocha. 2016-09-12. Feedback Motion Planning for Liquid Transfer using Supervised Learning. https://arxiv.org/abs/1609.03433

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