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.

翻译：本研究提出了一种新颖的昆虫启发的智能体，用于视觉点目标导航。该智能体融合了两种昆虫大脑结构的抽象模型，这两种结构分别与联想学习和路径整合相关。我们将Habitat点目标导航任务的正式基准与昆虫在已发现的食物位置和巢穴之间学习并优化绕障视觉引导路径的能力进行了类比。实验表明，这种简单的昆虫启发智能体在计算成本降低多个数量级的情况下，其性能可与近期的最先进模型相媲美。在更真实的模拟环境中的测试表明，该方法对扰动具有鲁棒性。