Physics-informed Reinforcement Learning for Perception and Reasoning
about Fluids
- URL: http://arxiv.org/abs/2203.05775v1
- Date: Fri, 11 Mar 2022 07:01:23 GMT
- Title: Physics-informed Reinforcement Learning for Perception and Reasoning
about Fluids
- Authors: Beatriz Moya, Alberto Badias, David Gonzalez, Francisco Chinesta,
Elias Cueto
- Abstract summary: We propose a physics-informed reinforcement learning strategy for fluid perception and reasoning from observations.
We develop a method for the tracking (perception) and analysis (reasoning) of any previously unseen liquid whose free surface is observed with a commodity camera.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Learning and reasoning about physical phenomena is still a challenge in
robotics development, and computational sciences play a capital role in the
search for accurate methods able to provide explanations for past events and
rigorous forecasts of future situations. We propose a physics-informed
reinforcement learning strategy for fluid perception and reasoning from
observations. As a model problem, we take the sloshing phenomena of different
fluids contained in a glass. Starting from full-field and high-resolution
synthetic data for a particular fluid, we develop a method for the tracking
(perception) and analysis (reasoning) of any previously unseen liquid whose
free surface is observed with a commodity camera. This approach demonstrates
the importance of physics and knowledge not only in data-driven (grey box)
modeling but also in the correction for real physics adaptation in low data
regimes and partial observations of the dynamics. The method here presented is
extensible to other domains such as the development of cognitive digital twins,
able to learn from observation of phenomena for which they have not been
trained explicitly.
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