Abstract Motion camouflage is a stealth behaviour by which an insect can appear stationary at a fixed point while approaching or escaping another moving insect. Although several approaches have been proposed to generate motion camouflage in simulated and real agents, the exact mechanisms insects use to perform this complex behaviour are not well understood, especially considering their limited perceptual and computational resources. This paper sheds light on the possible underlying control mechanisms insect might use to generate motion camouflage, by training and analysing a series of motion camouflage controllers using reinforcement learning. We first investigate through simulations the most relevant information available to the insect that can be used to perform motion camouflage and analyse the learnt controllers. The results of this analysis drove us to hypothesise two simpler control mechanisms which, we show, can also generate motion camouflage. The proposed controllers are an extension of proportional navigation, another interception technique found in nature, and therefore, both animal behaviours seem to be connected. Motion camouflage can lead, among others, to novel approaches to closely observe animals in the wild, record sports events or gather information in military operations without being noticed.
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