Pre-trained inference models from Astral — the open-source autonomous drone platform. These models are used in the Astral drone daemon running on Jetson Orin Nano hardware (M1-A quadcopter, M1-G ground rover).
All models are ONNX or GGUF format, ready for on-device inference without a training environment.
9-class aerial domain detector trained on 48,000 sim+real images across three training rounds. Classifies the visual domain of an aerial frame (indoor corridor, outdoor open, urban rooftop, etc.) to route perception appropriately.
Vision-language model fine-tuned with LoRA on drone-perspective navigation data. Used as the semantic target selector in Astral's modular autonomy stack — it answers "which object in this frame is the navigation goal?" and hands off to the metric depth module for 3D localization.
Run with llama.cpp or any GGUF-compatible runtime. Requires the paired mmproj file below.
Pair with: vlm_lora_v1_mmproj.gguf
Role in stack: semantic target selection (not end-to-end control)
Lightweight MLP reactive policy for collision avoidance and low-level flight stabilization. Runs alongside the VLM planner — the VLM sets the goal, the reactive policy handles moment-to-moment obstacle response.
Both files must be present in the same directory; policy_v1.onnx.data contains the weight tensors.
Input: state vector (normalize with policy_v1_state_norm.npy before inference)
Output: velocity command delta
policy_v1_state_norm.npy — State Normalization
Size: 224 B · Format: NumPy array
Mean/std normalization constants for policy_v1.onnx input. Load with numpy.load('policy_v1_state_norm.npy', allow_pickle=True).item() — returns {'mean': ..., 'std': ...}.
depth_v1.onnx — Monocular Depth Model
Size: 1.6 MB · Format: ONNX
Lightweight monocular depth estimation model for metric grounding. Converts the VLM's object identification into a 3D position estimate for the planner. Designed to run in real time on Jetson Orin Nano.
These models implement the separation principle: semantic understanding (VLM) is decoupled from metric geometry (depth) and low-level control (reactive policy). This architecture is why end-to-end VLMs fail drone navigation while the modular stack succeeds — detailed in Closing the Metric Gap.
