# TriHelper: Zero-Shot Object Navigation with Dynamic Assistance

arXiv: https://arxiv.org/abs/2403.15223 · HTML: https://mlsanity.com/paper/2403.15223/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2024.

## Built on by (most cited first, 25 of 25)

- [ImagineNav: Prompting Vision-Language Models as Embodied Navigator through Scene Imagination](https://mlsanity.com/paper/2410.09874/) (2024)
- [CogNav: Cognitive Process Modeling for Object Goal Navigation with LLMs](https://mlsanity.com/paper/2412.10439/) (2024)
- [Qwen-RobotNav Technical Report: A Scalable Navigation Model Designed for an Agentic Navigation System](https://mlsanity.com/paper/2606.18112/) (2026)
- [Is your VLM Sky-Ready? A Comprehensive Spatial Intelligence Benchmark for UAV Navigation](https://mlsanity.com/paper/2511.13269/) (2025)
- [Collaborative Instance Object Navigation: Leveraging Uncertainty-Awareness to Minimize Human-Agent Dialogues](https://mlsanity.com/paper/2412.01250/) (2024)
- [FutureNav: Unified World-Action Modeling for Vision-and-Language Navigation](https://mlsanity.com/paper/2606.30367/) (2026)
- [RATE-Nav: Region-Aware Termination Enhancement for Zero-shot Object Navigation with Vision-Language Models](https://mlsanity.com/paper/2506.02354/) (2025)
- [3DGSNav: Enhancing Vision-Language Model Reasoning for Object Navigation via Active 3D Gaussian Splatting](https://mlsanity.com/paper/2602.12159/) (2026)
- [LGR: LLM-Guided Ranking of Frontiers for Object Goal Navigation](https://mlsanity.com/paper/2503.20241/) (2025)
- [SemNav: A Model-Based Planner for Zero-Shot Object Goal Navigation Using Vision-Foundation Models](https://mlsanity.com/paper/2506.03516/) (2025)
- [SEF-MAP: Subspace-Decomposed Expert Fusion for Robust Multimodal HD Map Prediction](https://mlsanity.com/paper/2602.21589/) (2026)
- [MUVLA: Learning to Explore Object Navigation via Map Understanding](https://mlsanity.com/paper/2509.25966/) (2025)
- [ImagineNav++: Prompting Vision-Language Models as Embodied Navigator through Scene Imagination](https://mlsanity.com/paper/2512.17435/) (2025)
- [Schr"odinger's Navigator: Imagining an Ensemble of Futures for Zero-Shot Object Navigation](https://mlsanity.com/paper/2512.21201/) (2025)
- [ReasonNavi: Human-Inspired Global Map Reasoning for Zero-Shot Embodied Navigation](https://mlsanity.com/paper/2602.15864/) (2026)
- [Memory Over Maps: 3D Object Localization Without Reconstruction](https://mlsanity.com/paper/2603.20530/) (2026)
- [ConsistNav: Closing the Action Consistency Gap in Zero-Shot Object Navigation with Semantic Executive Control](https://mlsanity.com/paper/2605.09869/) (2026)
- [EvoMemNav: Efficient Self-Evolving Fine-Grained Memory for Zero-Shot Embodied Navigation](https://mlsanity.com/paper/2606.03509/) (2026)
- [LightNav-0: Eliciting VLM Spatial Intelligence for Generalist Embodied Navigation](https://mlsanity.com/paper/2608.30935/) (2026)
- [Improving Zero-Shot ObjectNav with Generative Communication](https://mlsanity.com/paper/2408.01877/) (2024)
- [Learning to Navigate Socially Through Proactive Risk Perception](https://mlsanity.com/paper/2510.07871/) (2025)
- [SR-Nav: Spatial Relationships Matter for Zero-shot Object Goal Navigation](https://mlsanity.com/paper/2603.18443/) (2026)
- [REST: Receding Horizon Explorative Steiner Tree for Zero-Shot Object-Goal Navigation](https://mlsanity.com/paper/2603.18624/) (2026)
- [RTNav: Towards Real-Time Zero-Shot Object Navigation](https://mlsanity.com/paper/2608.26496/) (2026)
- [GG-Nav: Goal-oriented Grounding Chain-of-Thought Elicits Reasoning in Navigation Foundation Models](https://mlsanity.com/paper/2610.14541/) (2026)

## Abstract (arXiv)

Navigating toward specific objects in unknown environments without additional training, known as Zero-Shot object navigation, poses a significant challenge in the field of robotics, which demands high levels of auxiliary information and strategic planning. Traditional works have focused on holistic solutions, overlooking the specific challenges agents encounter during navigation such as collision, low exploration efficiency, and misidentification of targets. To address these challenges, our work proposes TriHelper, a novel framework designed to assist agents dynamically through three primary navigation challenges: collision, exploration, and detection. Specifically, our framework consists of three innovative components: (i) Collision Helper, (ii) Exploration Helper, and (iii) Detection Helper. These components work collaboratively to solve these challenges throughout the navigation process. Experiments on the Habitat-Matterport 3D (HM3D) and Gibson datasets demonstrate that TriHelper significantly outperforms all existing baseline methods in Zero-Shot object navigation, showcasing superior success rates and exploration efficiency. Our ablation studies further underscore the effectiveness of each helper in addressing their respective challenges, notably enhancing the agent's navigation capabilities. By proposing TriHelper, we offer a fresh perspective on advancing the object navigation task, paving the way for future research in the domain of Embodied AI and visual-based navigation.