Memory-Augmented Reinforcement Learning for Image-Goal Navigation
This addresses the problem of poor generalization and sensor dependency in image-goal navigation for robotics, representing a strong domain-specific advancement.
The paper tackles image-goal navigation by proposing a memory-augmented reinforcement learning approach that uses an attention-based model with episodic memory, achieving state-of-the-art performance on the Gibson dataset using only RGB input without pose or depth sensors.
In this work, we present a memory-augmented approach for image-goal navigation. Earlier attempts, including RL-based and SLAM-based approaches have either shown poor generalization performance, or are heavily-reliant on pose/depth sensors. Our method is based on an attention-based end-to-end model that leverages an episodic memory to learn to navigate. First, we train a state-embedding network in a self-supervised fashion, and then use it to embed previously-visited states into the agent's memory. Our navigation policy takes advantage of this information through an attention mechanism. We validate our approach with extensive evaluations, and show that our model establishes a new state of the art on the challenging Gibson dataset. Furthermore, we achieve this impressive performance from RGB input alone, without access to additional information such as position or depth, in stark contrast to related work.