  Target-driven visual navigation is a challenging problem that requires a
robot to find the goal using only visual inputs. Many researchers have
demonstrated promising results using deep reinforcement learning (deep RL) on
various robotic platforms, but typical end-to-end learning is known for its
poor extrapolation capability to new scenarios. Therefore, learning a
navigation policy for a new robot with a new sensor configuration or a new
target still remains a challenging problem. In this paper, we introduce a
learning algorithm that enables rapid adaptation to new sensor configurations
or target objects with a few shots. We design a policy architecture with latent
features between perception and inference networks and quickly adapt the
perception network via meta-learning while freezing the inference network. Our
experiments show that our algorithm adapts the learned navigation policy with
only three shots for unseen situations with different sensor configurations or
different target colors. We also analyze the proposed algorithm by
investigating various hyperparameters.
