Learning to Learn How to Learn:Self-Adaptive Visual Navigation using Meta-Learning
Abstract
Learning is an inherently continuous phenomenon.
When humans learn a new task there is no explicit distinction between training and inference. As we learn a task,
we keep learning about it while performing the task. What
we learn and how we learn it varies during different stages
of learning. Learning how to learn and adapt is a key
property that enables us to generalize effortlessly to new
settings. This is in contrast with conventional settings in
machine learning where a trained model is frozen during
inference. In this paper we study the problem of learning to learn at both training and test time in the context
of visual navigation. A fundamental challenge in navigation is generalization to unseen scenes. In this paper we
propose a self-adaptive visual navigation method (SAVN)
which learns to adapt to new environments without any explicit supervision. Our solution is a meta-reinforcement
learning approach where an agent learns a self-supervised
interaction loss that encourages effective navigation. Our
experiments, performed in the AI2-THOR framework, show
major improvements in both success rate and SPL for visual
navigation in novel scenes. Our code and data are available
at: https://github.com/allenai/savn.