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.
翻译:物理现象的学习和推理仍然是机器人开发中的一项挑战,计算科学在寻找准确方法以解释过去的事件和对未来情况的严格预测方面发挥着资本作用。我们提出了一个热力学知情的积极学习战略,以便从观察中得出流体感知和推理。作为一个示范问题,我们采用玻璃中所含不同流体的悬浮现象。从一个特定流体的全场和高分辨率合成数据开始,我们开发一种方法,跟踪(感知)和分析(推理)任何以商品相机观测到自由表面的先前看不见的液体。这种方法不仅表明物理和知识在数据驱动(灰盒)模型中的重要性,而且也表明在对低数据系统进行实际物理适应和对动态进行部分观察的校正方面的重要性。我们提出的方法可以推广到其他领域,例如发展认知数字双胞体,能够从观测到它们没有得到明确培训的现象中学习。