# GeNIE: A Generalizable Navigation System for In-the-Wild Environments

arXiv: https://arxiv.org/abs/2506.17960 · HTML: https://mlsanity.com/paper/2506.17960/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 35 references that are in the map.
- First posted on arXiv 22 Jun 2025.

## Builds on (most cited first, 17 of 17)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [Habitat: A Platform for Embodied AI Research](https://mlsanity.com/paper/1904.01201/) (2019)
- [Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI](https://mlsanity.com/paper/2109.08238/) (2021)
- [Gibson Env: Real-World Perception for Embodied Agents](https://mlsanity.com/paper/1808.10654/) (2018)
- [Depth Pro: Sharp Monocular Metric Depth in Less Than a Second](https://mlsanity.com/paper/2410.02073/) (2024)
- [UniDepth: Universal Monocular Metric Depth Estimation](https://mlsanity.com/paper/2403.18913/) (2024)
- [Fast Traversability Estimation for Wild Visual Navigation](https://mlsanity.com/paper/2305.08510/) (2023)
- [Why Having 10,000 Parameters in Your Camera Model is Better Than Twelve](https://mlsanity.com/paper/1912.02908/) (2019)
- [Learning Off-Road Terrain Traversability with Self-Supervisions Only](https://mlsanity.com/paper/2305.18896/) (2023)
- [WayFASTER: a Self-Supervised Traversability Prediction for Increased Navigation Awareness](https://mlsanity.com/paper/2402.00683/) (2024)
- [Follow the Footprints: Self-supervised Traversability Estimation for Off-road Vehicle Navigation based on Geometric and Visual Cues](https://mlsanity.com/paper/2402.15363/) (2024)
- [Deep Learning Traversability Estimator for Mobile Robots in Unstructured Environments](https://mlsanity.com/paper/2105.10937/) (2021)
- [Deep Probabilistic Traversability with Test-time Adaptation for Uncertainty-aware Planetary Rover Navigation](https://mlsanity.com/paper/2409.00641/) (2024)
- [Dense Crowd Flow-Informed Path Planning](https://mlsanity.com/paper/2206.00705/) (2022)
- [BiC-MPPI: Goal-Pursuing, Sampling-Based Bidirectional Rollout Clustering Path Integral for Trajectory Optimization](https://mlsanity.com/paper/2410.06493/) (2024)

## Built on by (most cited first, 7 of 7)

- [From General Vision to Reliable Traversability Estimation: Adapting Vision Foundation Models for Unstructured Outdoor Environments](https://mlsanity.com/paper/2605.29565/) (2026)
- [TOPO-Bench: An Open-Source Topological Mapping Evaluation Framework with Quantifiable Perceptual Aliasing](https://mlsanity.com/paper/2510.04100/) (2025)
- [TARIC: Memory-Augmented Traversability-Aware Outdoor VLN under Interrupted Semantic Cues](https://mlsanity.com/paper/2605.31121/) (2026)
- [SAM-MT: Real-Time Interactive Multi-Target Video Segmentation](https://mlsanity.com/paper/2607.08688/) (2026)
- [ABot-AgentOS: A General Robotic Agent OS with Lifelong Multi-modal Memory](https://mlsanity.com/paper/2607.10350/) (2026)
- [ACME: A Multi-Cultural, Multi-Embodiment Social-Navigation Dataset](https://mlsanity.com/paper/2607.21964/) (2026)
- [Learning Social Navigation from Internet Videos in the Policy State Space](https://mlsanity.com/paper/2609.37476/) (2026)

## Abstract (arXiv)

Reliable navigation in unstructured, real-world environments remains a significant challenge for embodied agents, especially when operating across diverse terrains, weather conditions, and sensor configurations. In this paper, we introduce GeNIE (Generalizable Navigation System for In-the-Wild Environments), a robust navigation framework designed for global deployment. GeNIE integrates a generalizable traversability prediction model built on SAM2 with a novel path fusion strategy that enhances planning stability in noisy and ambiguous settings. We deployed GeNIE in the Earth Rover Challenge (ERC) at ICRA 2025, where it was evaluated across six countries spanning three continents. GeNIE took first place and achieved 79% of the maximum possible score, outperforming the second-best team by 17%, and completed the entire competition without a single human intervention. These results set a new benchmark for robust, generalizable outdoor robot navigation. We will release the codebase, pretrained model weights, and newly curated datasets to support future research in real-world navigation.