Emergence of Maps in the Memories of Blind Navigation Agents
Erik Wijmans, Manolis Savva, Irfan Essa, Stefan Lee, Ari S. Morcos,, Dhruv Batra

TL;DR
This study demonstrates that AI navigation agents with minimal sensing can implicitly develop internal maps and exhibit complex, intelligent behaviors, suggesting mapping as a fundamental mechanism in navigation.
Contribution
It shows that blind reinforcement learning agents can spontaneously develop internal spatial maps and exhibit advanced navigation behaviors without explicit mapping mechanisms.
Findings
Blind agents achieve ~95% success in new environments.
Agents remember around 1,000 steps of past experience.
Emergence of maps and collision detection neurons in representations.
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…
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Taxonomy
TopicsReinforcement Learning in Robotics · Robotic Path Planning Algorithms · Multimodal Machine Learning Applications
