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The batteries of many consumer products, including robots, are often both a substantial portion of the product's cost and commonly a first point of failure. Accurately predicting remaining battery life can lower costs by reducing unnecessary battery replacements. Unfortunately, battery dynamics are extremely complex, and we often lack the domain knowledge required to construct a model by hand. In this work, we take a data-driven approach and aim to learn a model of battery time-to-death fromdoi:10.1109/icra.2012.6225178 dblp:conf/icra/JosephDR12 fatcat:6hc7pos6q5eu5grxfaq3suqshu