AI

SuperNav: An Agentic Navigation System for Any Task in Any Scene

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 09/10/2026, 03:18:25 UTC.

Briefings

This article presents SuperNav, an agentic navigation system that uses a pretrained multimodal large language model (MLLM) to interpret requests and make decisions while delegating motion execution to specialized agents. The system integrates navigation skills, physical tools, and context management to support sustained navigation across diverse tasks and environments. SuperNav outperforms four baseline methods on instance-level, multi-object, and demand-driven tasks, with category-level evaluation on HM3D and deployment on a real quadruped robot demonstrating its applicability.

Key points

• SuperNav uses a pretrained MLLM with a specialized agent harness to interpret requests and make decisions, delegating motion execution to navigation tools.
• The system supports sustained navigation across different task requirements and environments through a unified visual-point interface connecting decisions to motion.

Context

Existing methods fine-tune multimodal large language models to predict navigation actions, limiting generalization due to training data coverage. SuperNav addresses this by letting the MLLM focus on interpretation while delegating motion execution to specialized agents.

Limitations

Evaluation is limited to instance-level, multi-object, and demand-driven tasks; category-level evaluation on HM3D and deployment on a real quadruped robot demonstrate applicability but do not cover all environmental scenarios.

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