# VLN on the Fly: An Onboard Vision-Language Navigation Stack for Aerial Robots

arXiv: https://arxiv.org/abs/2609.20191 · HTML: https://mlsanity.com/paper/2609.20191/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 21 references that are in the map.
- First posted on arXiv 7 Aug 2026.

## Builds on (most cited first, 16 of 16)

- [Hierarchical Open-Vocabulary 3D Scene Graphs for Language-Grounded Robot Navigation](https://mlsanity.com/paper/2403.17846/) (2024)
- [GNM: A General Navigation Model to Drive Any Robot](https://mlsanity.com/paper/2210.03370/) (2022)
- [AerialVLN: Vision-and-Language Navigation for UAVs](https://mlsanity.com/paper/2308.06735/) (2023)
- [Vision-and-Language Navigation Today and Tomorrow: A Survey in the Era of Foundation Models](https://mlsanity.com/paper/2407.07035/) (2024)
- [FLOWER: Democratizing Generalist Robot Policies with Efficient Vision-Language-Action Flow Policies](https://mlsanity.com/paper/2509.04996/) (2025)
- [EGO-Planner: An ESDF-free Gradient-based Local Planner for Quadrotors](https://mlsanity.com/paper/2008.08835/) (2020)
- [BEAST: Efficient Tokenization of B-Splines Encoded Action Sequences for Imitation Learning](https://mlsanity.com/paper/2506.06072/) (2025)
- [VLA-AN: An Efficient and Onboard Vision-Language-Action Framework for Aerial Navigation in Complex Environments](https://mlsanity.com/paper/2512.15258/) (2025)
- [See, Point, Fly: A Learning-Free VLM Framework for Universal Unmanned Aerial Navigation](https://mlsanity.com/paper/2509.22653/) (2025)
- [AutoFly: Vision-Language-Action Model for UAV Autonomous Navigation in the Wild](https://mlsanity.com/paper/2602.09657/) (2026)
- [AerialVLA: A Vision-Language-Action Model for UAV Navigation via Minimalist End-to-End Control](https://mlsanity.com/paper/2603.14363/) (2026)
- [RAPTOR: A Foundation Policy for Quadrotor Control](https://mlsanity.com/paper/2509.11481/) (2025)
- [Modular Safety Guardrails Are Necessary for Foundation-Model-Enabled Robots in the Real World](https://mlsanity.com/paper/2602.04056/) (2026)
- [SINGER: An Onboard Generalist Vision-Language Navigation Policy for Drones](https://mlsanity.com/paper/2509.18610/) (2025)
- [AirHunt: Bridging VLM Semantics and Continuous Planning for Efficient Aerial Object Navigation](https://mlsanity.com/paper/2601.12742/) (2026)
- [OnFly: Onboard Zero-Shot Aerial Vision-Language Navigation toward Safety and Efficiency](https://mlsanity.com/paper/2603.10682/) (2026)

## Abstract (arXiv)

Running vision-language navigation fully onboard an aerial robot is hard, since grounding, planning, and control must share limited compute and a single-stage error is difficult to isolate in flight. End-to-end aerial policies fuse these stages into one network, giving up the observability and safety checks a modular stack keeps available. We propose VLN on the Fly, an onboard stack that keeps grounding, planning, and control as separate, inspectable stages. A quantized VLM grounds an instruction to a coarse image cell, depth lifts it to a 3D goal, a fast B-spline planner returns a feasible trajectory, and a pretrained reinforcement learning policy tracks it to motor commands across quadrotors. Across 15 onboard flights over three everyday referents in a controlled indoor volume, the stack reaches the target in 13 of 15 trials with 5.72 cm mean goal error and 39.3% average GPU utilization. In 6 additional cluttered-environment trials, the stack tracks collision-free trajectories under onboard perception gating.