# ATLASv2: LLM-Guided Adaptive Landmark Acquisition and Navigation on the Edge

arXiv: https://arxiv.org/abs/2504.10784 · HTML: https://mlsanity.com/paper/2504.10784/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 15 Apr 2025.

## Builds on (most cited first, 3 of 3)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action](https://mlsanity.com/paper/2207.04429/) (2022)
- [SayNav: Grounding Large Language Models for Dynamic Planning to Navigation in New Environments](https://mlsanity.com/paper/2309.04077/) (2023)

## Built on by (most cited first, 3 of 3)

- [DREAM: Domain-aware Reasoning for Efficient Autonomous Underwater Monitoring](https://mlsanity.com/paper/2509.13666/) (2025)
- [EdgeNavMamba: Mamba Optimized Object Detection for Energy Efficient Edge Devices](https://mlsanity.com/paper/2510.14946/) (2025)
- [Nutri-ATLAS: Embodied Agent for Tabulated Lookup and Assistance for Smarter nutrition](https://mlsanity.com/paper/2609.32803/) (2026)

## Abstract (arXiv)

Autonomous systems deployed on edge devices face significant challenges, including resource constraints, real-time processing demands, and adapting to dynamic environments. This work introduces ATLASv2, a novel system that integrates a fine-tuned TinyLLM, real-time object detection, and efficient path planning to enable hierarchical, multi-task navigation and manipulation all on the edge device, Jetson Nano. ATLASv2 dynamically expands its navigable landmarks by detecting and localizing objects in the environment which are saved to its internal knowledge base to be used for future task execution. We evaluate ATLASv2 in real-world environments, including a handcrafted home and office setting constructed with diverse objects and landmarks. Results show that ATLASv2 effectively interprets natural language instructions, decomposes them into low-level actions, and executes tasks with high success rates. By leveraging generative AI in a fully on-board framework, ATLASv2 achieves optimized resource utilization with minimal prompting latency and power consumption, bridging the gap between simulated environments and real-world applications.