PEANUT: Predicting and Navigating to Unseen Targets

TL;DR

PEANUT introduces a simple, supervised learning approach for predicting unseen object locations in semantic maps, significantly improving ObjectNav performance without reinforcement learning.

Contribution

The paper presents a lightweight, supervised prediction model for unseen objects that enhances ObjectNav in novel environments, outperforming prior methods.

Findings

Achieves state-of-the-art results on HM3D and MP3D ObjectNav datasets.

Does not require reinforcement learning or additional data for training.

Effective in predicting unseen object locations using limited passively collected data.

Abstract

Efficient ObjectGoal navigation (ObjectNav) in novel environments requires an understanding of the spatial and semantic regularities in environment layouts. In this work, we present a straightforward method for learning these regularities by predicting the locations of unobserved objects from incomplete semantic maps. Our method differs from previous prediction-based navigation methods, such as frontier potential prediction or egocentric map completion, by directly predicting unseen targets while leveraging the global context from all previously explored areas. Our prediction model is lightweight and can be trained in a supervised manner using a relatively small amount of passively collected data. Once trained, the model can be incorporated into a modular pipeline for ObjectNav without the need for any reinforcement learning. We validate the effectiveness of our method on the HM3D and MP3D ObjectNav datasets. We find that it achieves the state-of-the-art on both datasets, despite not using any additional data for training.