Autonomous Frontier-Based Exploration with VLM Guidance
Aarush Aitha and Avideh Zakhor have proposed a new method for autonomous robotic exploration using Vision-Language Models (VLMs). This approach enhances decision-making in navigating unknown environments, improving map coverage by up to 24% compared to existing techniques. The system is lightweight and can be easily adapted to various robotic platforms with standard sensors and internet access.
- ▪The proposed exploration pipeline utilizes VLMs for high-level strategic decision-making.
- ▪Robots generate multimodal prompts that include current maps and visual imagery of potential paths.
- ▪The method has been validated in simulations across six indoor environments.
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Computer Science > Robotics arXiv:2605.23165 (cs) [Submitted on 22 May 2026] Title:Autonomous Frontier-Based Exploration with VLM Guidance Authors:Aarush Aitha, Avideh Zakhor View a PDF of the paper titled Autonomous Frontier-Based Exploration with VLM Guidance, by Aarush Aitha and Avideh Zakhor View PDF HTML (experimental) Abstract:Autonomous robotic exploration of unknown and hazardous environments, a long-standing challenge, can be significantly improved by leveraging the advanced reasoning of Vision-Language Models (VLMs). We introduce a novel exploration pipeline where a VLM performs high-level strategic decision-making, guiding a conventional low-level robotics control stack.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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