For Defense Drone OEMs

Ship swarm autonomy with your drones.

Stop building the autonomy stack from scratch. NeuHive trains multi-agent policies that export to PX4, ArduPilot, ONNX, and ROS 2 — so your customers get a swarm-ready airframe out of the box, and your team focuses on what only you can build: the hardware.

OEM InquiriesPlatform Features
The OEM problem

Your buyers want autonomy. Building it is brutal.

Hiring a multi-agent RL team
12–24 months and $2M+ of fully-loaded payroll before first flight.
Per-customer mission tuning
Every program office wants different terrain, weather, sensor stack, and rules of engagement.
Multiple flight-controller stacks
PX4 today, ArduPilot tomorrow, ROS 2 for the prime. Custom integration each time.
Proving autonomy works for the customer
Bench demos and slides don't survive a real test flight. Buyers want measured evidence.
What NeuHive delivers

The autonomy stack, ready to integrate.

Six things you get on day one. No partnership announcements required — just an API token and your flight-controller choice.

Trained policies, not a research project
Multi-Agent PPO with twenty UI-exposed hyperparameters, 100 built-in skill challenges across five tiers, and a curriculum that scales from single-drone hover to a 200-drone swarm. Production-live, not a paper.
Four export targets from one trained policy
ONNX (INT8 / FP16, dynamic batch). PX4 with MAVLink, SITL, Offboard, failsafe wired. ArduPilot via the pymavlink runner. ROS 2 with lifecycle node + action server. The trained swarm goes wherever your hardware goes.
Customer-facing environment builder
Thirteen configuration panels (physics, weather, sensors, comms, EW, patrol zones, rewards, difficulty, training, more) let your end-customers spin up mission-specific scenarios without touching code. You ship the platform; they ship the use case.
Sim-to-real measurement, not anecdotes
Customer uploads a real .ulg or .tlog from their flight test. NeuHive replays the commanded inputs through the simulator and produces RMSE, per-axis drift rate, and Jensen-Shannon divergence. Quantified evidence the trained policy actually transfers to your airframe.
Differentiable physics for system identification
Gradients flow through the simulation step. Fits physics parameters from real flight logs via gradient descent — no manual sweep, no brittle hand-tuning. The sim adapts to your motor response, your drag profile, your rotor wash.
Open architecture so you don't trade lock-in for capability
REST + WebSocket APIs, JSON config end-to-end, MOSA-aligned interfaces. Signed model exports (Ed25519 default, AWS KMS optional) so your customers can verify provenance without trusting the build pipeline.
Integration surface

Pick your flight controller. The trained policy follows.

One trained policy, four deployment targets. Train once. Ship to whatever hardware your customer ordered.

PX4
MAVLink integration, SITL bridge, Offboard mode, failsafe
ArduPilot
Companion package via pymavlink runner, GUIDED mode
ROS 2
Lifecycle node + action server, Humble + Iron, launch files
ONNX runtime
INT8 / FP16 quantization, dynamic batch, graph optimization
Mission realism

Train against the conditions your hardware actually faces.

The full simulator surface your customers can configure for their use case — same engine that trains the policy renders the live 3D preview, so what they see is what their drones learn from.

  • Custom CUDA physics: gravity, aerodynamic drag, motor dynamics with configurable response bandwidth, ground effect, rotor wash between stacked drones, Coulomb friction on contacts
  • Weather: Dryden turbulence + discrete gust events with configurable energy, frequency, axis
  • Sensors: per-channel noise, bias, dropout; GPS modes including degraded, spoofed, denied
  • Comms: packet loss, latency distribution, neighbor topology cap
  • Electronic warfare: named threat zones with jamming radius and GPS-denied regions
  • Domain randomization across seven axes (mass, drag, motor BW, sensor noise, latency, wind, spawn jitter)
  • Eight composable reward components with per-skill weights
  • Five-stage difficulty progression with auto-promotion gates
  • Obstacle library: Box, Wall, Cylindrical Tower, Tunnel, Bridge, Pipe, ISO Container, Scaffolding — plus custom GLB / USD / FBX uploads with proper AABB collision
The buyer's objection

“Sim-to-real never works.”

Your customer has heard it before. The honest answer: untuned simulation doesn't transfer. NeuHive's pipeline closes the loop with measured metrics on a real flight log from their airframe — not yours.

1. Customer flies a test mission with your drone.
2. They upload the .ulg or .tlog to NeuHive.
3. NeuHive replays the commanded inputs through the sim.
4. RMSE / drift rate / Jensen-Shannon divergence are computed.
5. Ranked DR recommendations apply with one click.
6. Retrain. Re-fly. Watch the gap close, measurably.

Let's talk integration.

Tell us your flight-controller stack and target swarm size. We'll send back a 30-minute integration walkthrough.

Email drones@neuhive.aiHow It Works