  Bees are among the master navigators of the insect world. Despite impressive
advances in robot navigation research, the performance of these insects is
still unrivaled by any artificial system in terms of training efficiency and
generalization capabilities, particularly considering the limited computational
capacity. On the other hand, computational principles underlying these
extraordinary feats are still only partially understood. The theoretical
framework of reinforcement learning (RL) provides an ideal focal point to bring
the two fields together for mutual benefit. In particular, we analyze and
compare representations of space in robot and insect navigation models through
the lens of RL, as the efficiency of insect navigation is likely rooted in an
efficient and robust internal representation, linking retinotopic (egocentric)
visual input with the geometry of the environment. While RL has long been at
the core of robot navigation research, current computational theories of insect
navigation are not commonly formulated within this framework, but largely as an
associative learning process implemented in the insect brain, especially in the
mushroom body (MB). Here we propose specific hypothetical components of the MB
circuit that would enable the implementation of a certain class of relatively
simple RL algorithms, capable of integrating distinct components of a
navigation task, reminiscent of hierarchical RL models used in robot
navigation. We discuss how current models of insect and robot navigation are
exploring representations beyond classical, complete map-like representations,
with spatial information being embedded in the respective latent
representations to varying degrees.
