Emergence of Maps in the Memories of Blind Navigation Agents
ICLR• 2023
Abstract
Animal navigation research posits that organisms build and maintain internal
spatial representations, or maps, of their environment. We ask if machines --
specifically, artificial intelligence (AI) navigation agents -- also build
implicit (or 'mental') maps. A positive answer to this question would (a)
explain the surprising phenomenon in recent literature of ostensibly map-free
neural-networks achieving strong performance, and (b) strengthen the evidence
of mapping as a fundamental mechanism for navigation by intelligent embodied
agents, whether they be biological or artificial. Unlike animal navigation, we
can judiciously design the agent's perceptual system and control the learning
paradigm to nullify alternative navigation mechanisms. Specifically, we train
'blind' agents -- with sensing limited to only egomotion and no other sensing
of any kind -- to perform PointGoal navigation ('go to x, y')
via reinforcement learning. Our agents are composed of navigation-agnostic
components (fully-connected and recurrent neural networks), and our
experimental setup provides no inductive bias towards mapping. Despite these
harsh conditions, we find that blind agents are (1) surprisingly effective
navigators in new environments (~95% success); (2) they utilize memory over
long horizons (remembering ~1,000 steps of past experience in an episode); (3)
this memory enables them to exhibit intelligent behavior (following walls,
detecting collisions, taking shortcuts); (4) there is emergence of maps and
collision detection neurons in the representations of the environment built by
a blind agent as it navigates; and (5) the emergent maps are selective and task
dependent (e.g. the agent 'forgets' exploratory detours). Overall, this paper
presents no new techniques for the AI audience, but a surprising finding, an
insight, and an explanation.