SuperNav: An Agentic Navigation System for Any Task in Any Scene
- Published
- Source
- arXiv
- Paper number
- 1176
- Field
- Robotics
- arXiv ID
- 2610.12126
Key points
- It achieved generalization with no navigation-specific fine-tuning, simply by equipping a pretrained MLLM with a dedicated agent harness.
- The model issues movement commands through a visual-point interface where it clicks the destination directly on an image, and revises decisions from execution feedback.
- It reached 78.00% success on single-object navigation, 44 percentage points ahead of the strongest baseline UniNaVid at 34.00%.
- Without any OVON training or adaptation it recorded a 73.33% success rate over 120 HM3D-OVON episodes.
- It validated real-world applicability by deploying on an actual quadruped robot as well as in simulation.
Paper links
External research summaries. These are not HDATF publications or measured product results.