An Efficient Insect-inspired Approach for Visual Point-goal Navigation
This addresses the problem of efficient and robust navigation for robotics or AI systems, though it appears incremental as it adapts known biological principles to an existing benchmark.
The paper tackled visual point-goal navigation by developing an insect-inspired agent that combines models of insect brain structures for associative learning and path integration, achieving performance comparable to recent SOTA models with significantly lower computational cost.
In this work we develop a novel insect-inspired agent for visual point-goal navigation. This combines abstracted models of two insect brain structures that have been implicated, respectively, in associative learning and path integration. We draw an analogy between the formal benchmark of the Habitat point-goal navigation task and the ability of insects to learn and refine visually guided paths around obstacles between a discovered food location and their nest. We demonstrate that the simple insect-inspired agent exhibits performance comparable to recent SOTA models at many orders of magnitude less computational cost. Testing in a more realistic simulated environment shows the approach is robust to perturbations.