Grid cells in the hippocampal formation are a valuable system to study both for neuroscientists and for neural network researchers, as these neurons present both a window into higher-level cognitive processes such as navigation, as well as inspiration for how to build artificial neural navigation systems. Grid cells are believed to represent an animal’s coordinates in two-dimensional space in a general fashion, useable for geometric computations by downstream neural networks, and earlier neural models have indeed shown how grid cells can be decoded for navigational purposes. However, accumulating evidence shows that grid cells are not as stable as assumed by models, but that they exhibit various geometric distortions depending on time and place. This presents a challenge to grid cell decoding models, which mainly separate into “nested” and “combinatorial” ones. Here we present a new and simplified version of a nested grid cell decoder, demonstrate that this decoder can cope with distortions, and show how this relates to a fundamental property of nested grid cell decoding. By providing positive proof that a nested decoder can navigate with distorted grid cells, we hope to inspire further neuroscientific investigation into the biological plausibility of different models for grid cell-based navigation.