# Navigating to Objects in Unseen Environments by Distance Prediction

arXiv: https://arxiv.org/abs/2202.03735 · HTML: https://mlsanity.com/paper/2202.03735/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 62 references that are in the map.
- First posted on arXiv 8 Feb 2022.

## Builds on (most cited first, 30 of 36)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Matterport3D: Learning from RGB-D Data in Indoor Environments](https://mlsanity.com/paper/1709.06158/) (2017)
- [Habitat: A Platform for Embodied AI Research](https://mlsanity.com/paper/1904.01201/) (2019)
- [On Evaluation of Embodied Navigation Agents](https://mlsanity.com/paper/1807.06757/) (2018)
- [Object Goal Navigation using Goal-Oriented Semantic Exploration](https://mlsanity.com/paper/2007.00643/) (2020)
- [Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning](https://mlsanity.com/paper/1609.05143/) (2016)
- [Learning to Explore using Active Neural SLAM](https://mlsanity.com/paper/2004.05155/) (2020)
- [ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to Objects](https://mlsanity.com/paper/2006.13171/) (2020)
- [Cognitive Mapping and Planning for Visual Navigation](https://mlsanity.com/paper/1702.03920/) (2017)
- [Semi-parametric Topological Memory for Navigation](https://mlsanity.com/paper/1803.00653/) (2018)
- [Learning to Navigate in Complex Environments](https://mlsanity.com/paper/1611.03673/) (2016)
- [Neural Topological SLAM for Visual Navigation](https://mlsanity.com/paper/2005.12256/) (2020)
- [Value Iteration Networks](https://mlsanity.com/paper/1602.02867/) (2016)
- [Neural Episodic Control](https://mlsanity.com/paper/1703.01988/) (2017)
- [Learning Exploration Policies for Navigation](https://mlsanity.com/paper/1903.01959/) (2019)
- [Building Generalizable Agents with a Realistic and Rich 3D Environment](https://mlsanity.com/paper/1801.02209/) (2018)
- [Episodic Curiosity through Reachability](https://mlsanity.com/paper/1810.02274/) (2018)
- [Scene Memory Transformer for Embodied Agents in Long-Horizon Tasks](https://mlsanity.com/paper/1903.03878/) (2019)
- [Occupancy Anticipation for Efficient Exploration and Navigation](https://mlsanity.com/paper/2008.09285/) (2020)
- [Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning](https://mlsanity.com/paper/1812.00971/) (2018)
- [Neural Map: Structured Memory for Deep Reinforcement Learning](https://mlsanity.com/paper/1702.08360/) (2017)
- [Visual Representations for Semantic Target Driven Navigation](https://mlsanity.com/paper/1805.06066/) (2018)
- [Control of Memory, Active Perception, and Action in Minecraft](https://mlsanity.com/paper/1605.09128/) (2016)
- [Learning to Map for Active Semantic Goal Navigation](https://mlsanity.com/paper/2106.15648/) (2021)
- [Bayesian Relational Memory for Semantic Visual Navigation](https://mlsanity.com/paper/1909.04306/) (2019)
- [SSCNav: Confidence-Aware Semantic Scene Completion for Visual Semantic Navigation](https://mlsanity.com/paper/2012.04512/) (2020)
- [Visual Navigation with Spatial Attention](https://mlsanity.com/paper/2104.09807/) (2021)
- [Learning to Act by Predicting the Future](https://mlsanity.com/paper/1611.01779/) (2016)

## Built on by (most cited first, 16 of 16)

- [Navigating to Objects in the Real World](https://mlsanity.com/paper/2212.00922/) (2022)
- [OVRL-V2: A simple state-of-art baseline for ImageNav and ObjectNav](https://mlsanity.com/paper/2303.07798/) (2023)
- [Learning Navigational Visual Representations with Semantic Map Supervision](https://mlsanity.com/paper/2307.12335/) (2023)
- [Object Goal Navigation with Recursive Implicit Maps](https://mlsanity.com/paper/2308.05602/) (2023)
- [TopV-Nav: Unlocking the Top-View Spatial Reasoning Potential of MLLM for Zero-shot Object Navigation](https://mlsanity.com/paper/2411.16425/) (2024)
- [Tag Map: A Text-Based Map for Spatial Reasoning and Navigation with Large Language Models](https://mlsanity.com/paper/2409.15451/) (2024)
- [ENTL: Embodied Navigation Trajectory Learner](https://mlsanity.com/paper/2304.02639/) (2023)
- [MOPA: Modular Object Navigation with PointGoal Agents](https://mlsanity.com/paper/2304.03696/) (2023)
- [Personalized Instance-based Navigation Toward User-Specific Objects in Realistic Environments](https://mlsanity.com/paper/2410.18195/) (2024)
- [Perception Matters: Enhancing Embodied AI with Uncertainty-Aware Semantic Segmentation](https://mlsanity.com/paper/2408.02297/) (2024)
- [Learning to Explore Informative Trajectories and Samples for Embodied Perception](https://mlsanity.com/paper/2303.10936/) (2023)
- [Distilling LLM Prior to Flow Model for Generalizable Agent's Imagination in Object Goal Navigation](https://mlsanity.com/paper/2508.09423/) (2025)
- [IN-Sight: Interactive Navigation through Sight](https://mlsanity.com/paper/2408.00343/) (2024)
- [SEA: Semantic Map Prediction for Active Exploration of Uncertain Areas](https://mlsanity.com/paper/2510.19766/) (2025)
- [FOM-Nav: Frontier-Object Maps for Object Goal Navigation](https://mlsanity.com/paper/2512.01009/) (2025)
- [ImagineNav++: Prompting Vision-Language Models as Embodied Navigator through Scene Imagination](https://mlsanity.com/paper/2512.17435/) (2025)

## Abstract (arXiv)

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