ScanDreamer: A Map-Grounded World Model for Quadrotor Navigation
Overview
This paper introduces ScanDreamer, a world model designed for quadrotor navigation in cluttered, unknown environments.
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Reported quantity · episodes: 1560 other · reported trials
“tes the quadrotor pose over a frozen map and derives progress and wall-contact signals from the mapped geometry. We train ScanDreamer for 0.8 million environment steps (5.5 h on one GPU) and evaluate it in NVIDIA Isaac Sim over 1560 episodes spanning unseen layouts and parameter ranges. Across three seeds (mean $\pm$ SD), ScanDreamer achieves 96.3 $\pm$ 0.3% success with 3.6 $\pm$ 0.3% collision, compared with 59.6 $”
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Artifact availability · code: available
“\pm$ 3.0% success and 28.8 $\pm$ 2.8% collision for AirDreamer at the same online-interaction budget. Removing the forward-cone penalty increases collisions to 9.9 $\pm$ 3.1%. These results show that combining explicit map memory, map-grounded imagination, and perception-aware motion substantially improves navigation performance and data efficiency. The source code is available at https://github.com/WANGSHUAIzzzz/Sca”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalScanDreamer: A Map-Grounded World Model for Quadrotor Navigation
This paper introduces ScanDreamer, a world model designed for quadrotor navigation in cluttered, unknown environments.
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