  State-of-the-art approaches to ObjectGoal navigation rely on reinforcement
learning and typically require significant computational resources and time for
learning. We propose Potential functions for ObjectGoal Navigation with
Interaction-free learning (PONI), a modular approach that disentangles the
skills of `where to look?' for an object and `how to navigate to (x, y)?'. Our
key insight is that `where to look?' can be treated purely as a perception
problem, and learned without environment interactions. To address this, we
propose a network that predicts two complementary potential functions
conditioned on a semantic map and uses them to decide where to look for an
unseen object. We train the potential function network using supervised
learning on a passive dataset of top-down semantic maps, and integrate it into
a modular framework to perform ObjectGoal navigation. Experiments on Gibson and
Matterport3D demonstrate that our method achieves the state-of-the-art for
ObjectGoal navigation while incurring up to 1,600x less computational cost for
training. Code and pre-trained models are available:
https://vision.cs.utexas.edu/projects/poni/
