Title: One-Shot Reinforcement Learning for Robot Navigation with Interactive Replay,
Abstract: Recently, model-free reinforcement learning algorithms have been shown to
solve challenging problems by learning from extensive interaction with the
environment. A significant issue with transferring this success to the robotics
domain is that interaction with the real world is costly, but training on
limited experience is prone to overfitting. We present a method for learning to
navigate, to a fixed goal and in a known environment, on a mobile robot. The
robot leverages an interactive world model built from a single traversal of the
environment, a pre-trained visual feature encoder, and stochastic environmental
augmentation, to demonstrate successful zero-shot transfer under real-world
environmental variations without fine-tuning.