Research
Astral treats autonomous uncrewed systems as a systems problem: perception, geometry, planning, safety, simulation fidelity, and honest evaluation—whether the robot flies, drives, or does both. These papers and notes are the scientific backbone behind our open software, datasets, benchmarks, and vehicle programs.
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.