Navigation tasks in photorealistic 3D environments are challenging because
they require perception and effective planning under partial observability.
Recent work shows that map-like memory is useful for long-horizon navigation
tasks. However, a focused investigation of the impact of maps on navigation
tasks of varying complexity has not yet been performed. We propose the multiON
task, which requires navigation to an episode-specific sequence of objects in a
realistic environment. MultiON generalizes the ObjectGoal navigation task and
explicitly tests the ability of navigation agents to locate previously observed
goal objects. We perform a set of multiON experiments to examine how a variety
of agent models perform across a spectrum of navigation task complexities. Our
experiments show that: i) navigation performance degrades dramatically with
escalating task complexity; ii) a simple semantic map agent performs
surprisingly well relative to more complex neural image feature map agents; and
iii) even oracle map agents achieve relatively low performance, indicating the
potential for future work in training embodied navigation agents using maps.
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本文探讨了在三维环境中进行导航任务的挑战以及地图类记忆对导航任务的影响，提出了新的 MultiON 任务，通过一系列实验考察了不同复杂度任务下代理模型的表现，发现简单语义地图代理的表现相对更优，但神经图像特征地图代理和 oracle 地图代理也存在局限性，可能需要进一步的训练和研究。