Navigating to Objects in Unseen Environments by Distance Prediction

TL;DR

This paper introduces a distance prediction approach for Object Goal Navigation in unseen environments, enabling agents to efficiently explore and reach target objects without prior maps, demonstrated through simulation and real-world experiments.

Contribution

The paper presents a novel method that predicts distances to objects using semantic cues, improving navigation success and efficiency in unseen environments.

Findings

Outperforms baselines in success rate and efficiency in simulation.

Generalizes well to real-world robot navigation.

Uses semantic maps and distance estimation for mid-term goal selection.

Abstract

Object Goal Navigation (ObjectNav) task is to navigate an agent to an object category in unseen environments without a pre-built map. In this paper, we solve this task by predicting the distance to the target using semantically-related objects as cues. Based on the estimated distance to the target object, our method directly choose optimal mid-term goals that are more likely to have a shorter path to the target. Specifically, based on the learned knowledge, our model takes a bird's-eye view semantic map as input, and estimates the path length from the frontier map cells to the target object. With the estimated distance map, the agent could simultaneously explore the environment and navigate to the target objects based on a simple human-designed strategy. Empirical results in visually realistic simulation environments show that the proposed method outperforms a wide range of baselines on success rate and efficiency. Real-robot experiment also demonstrates that our method generalizes well to the real world. Video at https://www.youtube.com/watch?v=R79pWVGFKS4