Research
Astral treats autonomous drones as a systems problem: perception, geometry, planning, safety, simulation fidelity, and honest evaluation. These papers and notes are the scientific backbone behind our open software, datasets, and benchmarks.
NeurIPS 2026 Datasets & Benchmarks track (submission)
Introduces Yonder, a multi-million-frame drone-perspective indoor dataset with rich sensing, and shows why offline detection gains can fail to translate to closed-loop navigation when training and evaluation simulators disagree geometrically.
Technical report
Large-scale closed-loop benchmark across many VLMs, decomposing failures into semantic understanding versus metric spatial grounding, and a modular architecture that closes the gap on operational commands while prioritizing collision-free flight.
Technical report
An eighteen-iteration engineering log: improving a modular autonomy stack in aggregate, scaling detector fine-tuning with large synthetic data, diagnosing a cross-simulator localization gap, and characterizing exploration and planning as the next bottlenecks.
Technical report
Controlled swarm simulations up to 1,000 agents comparing sensing stacks and coordination architectures, with a focus on when ultra-wideband ranging becomes necessary as fleet scale and environment difficulty increase.
Technical note
Adds Gemma 4 to the same Isaac Sim closed-loop benchmark and compares end-to-end goal prediction against modular deployment of the same weights as a semantic target selector, illustrating the leverage of the separation principle.