About SAVN

SAVN Teaser Figure
Fig. 1. Traditional navigation approaches freeze the model during inference (top row); this may result in difficulties generalizing to unseen environments. In this paper, we propose a meta-reinforcement learning approach for navigation, where the agent learns to adapt in a self-supervised manner (bottom row). In this example, the agent learns to adapt itself when it collides with an object once and acts correctly afterwards. In contrast, a standard solution (top row) may make multiple mistakes of the same kind when performing the task.

When humans learn a new task there is no clear distinction between training and inference. Even after we explicitly learn a task, we keep learning while we perform. In this project we study the problem of learning to learn without supervision in the context of visual navigation. Our agent learns how to adapt itself to environments by interacting with them. Accordingly, the agent may continue learning even during inference.


Learning to Learn How to Learn: Self-Adaptive Visual Navigation using Meta-Learning

Mitchell Wortsman, Kiana Ehsani, Mohammad Rastegari, Ali Farhadi, and Roozbeh Mottaghi CVPR  2019