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arXiv · 1711.10137

One-Shot Reinforcement Learning for Robot Navigation with Interactive Replay

Jake Bruce·Niko Suenderhauf·Piotr Mirowski·Raia Hadsell·Michael Milford
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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.

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Jake Bruce, Niko Suenderhauf, Piotr Mirowski, Raia Hadsell, Michael Milford. 2017-11-29. One-Shot Reinforcement Learning for Robot Navigation with Interactive Replay. https://arxiv.org/abs/1711.10137

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