This special issue on “Complex Spatial Navigation in Animals, Computational Models and Neuro-inspired Robots” has its origins in a workshop held Sept 28, 2018, in Lyon, France, organized by Peter Ford Dominey, Jean-Marc Fellous, and Alfredo Weitzenfeld sponsored by a joint NSF and ANR CRCNS grant (award 1429937). The goal of the workshopwas to discuss the latest advances in understanding the neural mechanisms of complex spatial navigation using experimental studies, computational modeling and robotics evaluations, giving emphasis on studies that relate at least 2 of the 3 techniques (animals, computational neuroscience and neuro-robotics). This issue brings together contributions from workshop participants as well as from additional researchers to discuss the study of spatial cognition in complex environments. The human brain is one of the most complex biological computing device, with over 300 billion parallel processors, linked with over 30 trillion connections. How does one study such a phenomenal machine? The classic scientific approach has always been to place biological complexity in a simple environment where most of the features are controlled and easily manipulated. Then, for each set of environmental parameters, repeatedly, painstakingly, present well-designed sensory inputs and measure relevant behavioral outputs. The hope is that, on average, some interesting relationships between inputs and outputs will emerge, and that these relationships will give insights into the underlying neural computations. This method necessarily makes a number of strong assumptions, not the least of which is that the complexity of a system can be understood by collecting data