# SINGER: An Onboard Generalist Vision-Language Navigation Policy for Drones

arXiv: https://arxiv.org/abs/2509.18610 · HTML: https://mlsanity.com/paper/2509.18610/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 33 of its 36 references that are in the map.
- First posted on arXiv 23 Sep 2025.

## Placed in

- Multimodal LLMs / vision-language models / [Embodied and 3D vision-language models](https://mlsanity.com/mllm/embodied-and-3d-vision-language-models/)
- Vision-language-action models / [Aerial and drone VLA](https://mlsanity.com/vla/aerial-and-drone-vla/)

## Builds on (most cited first, 30 of 33)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control](https://mlsanity.com/paper/2307.15818/) (2023)
- [OpenVLA: An Open-Source Vision-Language-Action Model](https://mlsanity.com/paper/2406.09246/) (2024)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [π0: A Vision-Language-Action Flow Model for General Robot Control](https://mlsanity.com/paper/2410.24164/) (2024)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Open X-Embodiment: Robotic Learning Datasets and RT-X Models](https://mlsanity.com/paper/2310.08864/) (2023)
- [DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset](https://mlsanity.com/paper/2403.12945/) (2024)
- [CLIPort: What and Where Pathways for Robotic Manipulation](https://mlsanity.com/paper/2109.12098/) (2021)
- [Nerfstudio: A Modular Framework for Neural Radiance Field Development](https://mlsanity.com/paper/2302.04264/) (2023)
- [Image Segmentation Using Text and Image Prompts](https://mlsanity.com/paper/2112.10003/) (2021)
- [LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action](https://mlsanity.com/paper/2207.04429/) (2022)
- [Visual Language Maps for Robot Navigation](https://mlsanity.com/paper/2210.05714/) (2022)
- [ViNT: A Foundation Model for Visual Navigation](https://mlsanity.com/paper/2306.14846/) (2023)
- [CoWs on Pasture: Baselines and Benchmarks for Language-Driven Zero-Shot Object Navigation](https://mlsanity.com/paper/2203.10421/) (2022)
- [Distilled Feature Fields Enable Few-Shot Language-Guided Manipulation](https://mlsanity.com/paper/2308.07931/) (2023)
- [Deep Drone Racing: From Simulation to Reality with Domain Randomization](https://mlsanity.com/paper/1905.09727/) (2019)
- [acados: a modular open-source framework for fast embedded optimal control](https://mlsanity.com/paper/1910.13753/) (2019)
- [Splat-Nav: Safe Real-Time Robot Navigation in Gaussian Splatting Maps](https://mlsanity.com/paper/2403.02751/) (2024)
- [Splat-MOVER: Multi-Stage, Open-Vocabulary Robotic Manipulation via Editable Gaussian Splatting](https://mlsanity.com/paper/2405.04378/) (2024)
- [UAV-Flow Colosseo: A Real-World Benchmark for Flying-on-a-Word UAV Imitation Learning](https://mlsanity.com/paper/2505.15725/) (2025)
- [Follow Anything: Open-set detection, tracking, and following in real-time](https://mlsanity.com/paper/2308.05737/) (2023)
- [RaceVLA: VLA-based Racing Drone Navigation with Human-like Behaviour](https://mlsanity.com/paper/2503.02572/) (2025)
- [SEEK: Semantic Reasoning for Object Goal Navigation in Real World Inspection Tasks](https://mlsanity.com/paper/2405.09822/) (2024)
- [Grounded Vision-Language Navigation for UAVs with Open-Vocabulary Goal Understanding](https://mlsanity.com/paper/2506.10756/) (2025)
- [SOUS VIDE: Cooking Visual Drone Navigation Policies in a Gaussian Splatting Vacuum](https://mlsanity.com/paper/2412.16346/) (2024)
- [Gaussian Splatting to Real World Flight Navigation Transfer with Liquid Networks](https://mlsanity.com/paper/2406.15149/) (2024)
- [GRaD-Nav++: Vision-Language Model Enabled Visual Drone Navigation with Gaussian Radiance Fields and Differentiable Dynamics](https://mlsanity.com/paper/2506.14009/) (2025)
- [Finding Things in the Unknown: Semantic Object-Centric Exploration with an MAV](https://mlsanity.com/paper/2302.14569/) (2023)

## Built on by (most cited first, 6 of 6)

- [π, But Make It Fly: Physics-Guided Transfer of VLA Models to Aerial Manipulation](https://mlsanity.com/paper/2603.25038/) (2026)
- [LEGS: Fine-Tuning Teleop-Free VLAs for Humanoid Loco-manipulation in an Embodied Gaussian Splatting World](https://mlsanity.com/paper/2606.01458/) (2026)
- [A Comprehensive Survey and Systematic Real-World Evaluation of Embodied Vision-and-Language Navigation](https://mlsanity.com/paper/2607.09792/) (2026)
- [LiteVLA-H: Dual-Rate Vision-Language-Action Inference for Onboard Aerial Guidance and Semantic Perception](https://mlsanity.com/paper/2605.00884/) (2026)
- [PEACE: A Planner-Executor Agent with Constraint Enforcement for UAVs](https://mlsanity.com/paper/2606.00104/) (2026)
- [VLN on the Fly: An Onboard Vision-Language Navigation Stack for Aerial Robots](https://mlsanity.com/paper/2609.20191/) (2026)

## Abstract (arXiv)

Large vision-language models have driven remarkable progress in open-vocabulary robot policies, e.g., generalist robot manipulation policies, that enable robots to complete complex tasks specified in natural language. Despite these successes, open-vocabulary autonomous drone navigation remains an unsolved challenge due to the scarcity of large-scale demonstrations, real-time control demands of drones for stabilization, and lack of reliable external pose estimation modules. In this work, we present SINGER for language-guided autonomous drone navigation in the open world using only onboard sensing and compute. To train robust, open-vocabulary navigation policies, SINGER leverages three central components: (i) a photorealistic language-embedded flight simulator with minimal sim-to-real gap using Gaussian Splatting for efficient data generation, (ii) an RRT-inspired multi-trajectory generation expert for collision-free navigation demonstrations, and these are used to train (iii) a lightweight end-to-end visuomotor policy for real-time closed-loop control. Through extensive hardware flight experiments, we demonstrate superior zero-shot sim-to-real transfer of our policy to unseen environments and unseen language-conditioned goal objects. When trained on ~700k-1M observation action pairs of language conditioned visuomotor data and deployed on hardware, SINGER outperforms a velocity-controlled semantic guidance baseline by reaching the query 23.33% more on average, and maintains the query in the field of view 16.67% more on average, with 10% fewer collisions.