One-Shot Reinforcement Learning for Robot Navigation with Interactive Replay
Abstrak
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.
Penulis (5)
Jake Bruce
Niko Suenderhauf
Piotr Mirowski
Raia Hadsell
Michael Milford
Akses Cepat
- Tahun Terbit
- 2017
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓