Dream to Fly: Model-Based Reinforcement Learning for Vision-Based Drone Flight

[2501.14377] Dream to Fly: Model-Based Reinforcement Learning for Vision-Based Drone Flight Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Robotics arXiv:2501.14377 (cs) [Submitted on 24 Jan 2025 ( v1 ), last revised 10 Apr 2026 (this version, v2)] Title: Dream to Fly: Model-Based Reinforcement Learning for Vision-Based Drone Flight Authors: Angel Romero , Ashwin Shenai , Ismail Geles , Elie Aljalbout , Davide Scaramuzza View a PDF of the paper titled Dream to Fly: Model-Based Reinforcement Learning for Vision-Based Drone Flight, by Angel Romero and 4 other authors View PDF HTML (experimental) Abstract: Autonomous drone racing has risen as a challenging robotic benchmark for testing the limits of learning, perception, planning, and control. Expert human pilots are able to fly a drone through a race track by mapping pixels from a single camera directly to control commands. Recent works in autonomous drone racing attempting direct pixel-to-commands control policies have relied on either intermediate representations that simplify the observation space or performed extensive bootstrapping using Imitation Learning (IL). This paper leverages DreamerV3 to train visuomotor policies capable of agile flight through a racetrack using only pixels as observations. In contrast to model-free methods like PPO or SAC, which are sample-inefficient and struggle in this setting, our approach acquires drone racing skills from pixels. Notably, a perception-aware behaviour of actively steering the camera toward texture-rich gate regions emerges without the need of handcrafted reward terms for the viewing direction. Our experiments show in both, simulation and real-world flight using a hardware-in-the-loop setup with rendered image observations, how the proposed approach can be deployed on real quadrotors at speeds of up to 9 m/s. These results advance the state of pixel-based autonomous flight and demonstrate that MBRL offers a promising path for real-world robotics research. Comments: 8 pages, 6 Figures, accepted to IEEE ICRA 2026 Subjects: Robotics (cs.RO) ACM classes: I.2.9; I.2.10; I.2.6 Cite as: arXiv:2501.14377 [cs.RO] (or arXiv:2501.14377v2 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2501.14377 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Angel Romero [ view email ] [v1] Fri, 24 Jan 2025 10:24:39 UTC (8,710 KB) [v2] Fri, 10 Apr 2026 10:10:25 UTC (4,973 KB) Full-text links: Access Paper: View a PDF of the paper titled Dream to Fly: Model-Based Reinforcement Learning for Vision-Based Drone Flight, by Angel Romero and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.RO < prev | next > new | recent | 2025-01 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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