Critical Sampling for Data-Driven Modeling of Unknown Dynamical Systems
Keywords: Critical Sampling, Lifelong Learning, Dynamical Systems
TL;DR: We propose a critical sampling scheme for accurately learning the evolution behaviors of unknown dynamical systems.
Abstract: When a robot is exploring an unknown dynamical system, we often face the following important question: what is the minimum number of samples needed for effective learning of its governing laws and accurate prediction of its future evolution behavior, and how to select these critical samples? In this work, we propose to explore this problem based on a design approach. Starting from a small initial set of samples, we adaptively discover critical samples to achieve increasingly accurate learning of the system evolution. We establish a multi-step reciprocal prediction network where forward and backward evolution networks are designed to learn the temporal evolution behavior in the forward and backward time directions, respectively. Very interestingly, we find that the desired network modeling error is highly correlated with the multi-step reciprocal prediction error, which can be directly computed from the current system state. This allows us to perform a dynamic selection of critical samples from regions with high network modeling errors for dynamical systems. Our extensive experimental results demonstrate that our proposed method is able to dramatically reduce the number of samples needed for effective learning and accurate prediction of evolution behaviors of unknown dynamical systems by up to hundreds of times.
Submission Number: 11
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