A Thermodynamics-informed Active Learning Approach to Perception and Reasoning about Fluids

TL;DR

This paper introduces a thermodynamics-informed active learning method for fluid perception and reasoning, enabling robots to understand and predict fluid dynamics from limited observational data, with potential applications in digital twins.

Contribution

It presents a novel active learning approach that integrates thermodynamics principles for perception and reasoning about fluids from partial observations.

Findings

Effective tracking and analysis of unseen liquids from camera data

Physics-informed correction improves low-data regime performance

Method extensible to other physical phenomena and digital twin applications

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 thermodynamics-informed active 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 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.