Astrabeam: robots navigate where infrastructures fail

Physical AI requires robust autonomy for robots to work in the real world, not just in ideal conditions.

Today's robots rely heavily on GPS, VIO and SLAM-based navigation, which fail in unstructured, cluttered, degraded or contested environments. We're building the next generation of autonomy and perception systems by combining AI-driven sensor fusion, edge processing and robotics control.

Our platform integrates novel radar, computer vision, edge processing and motion control technologies to enable robust, all-environment autonomy and perception systems across robotics, industrial operations and defense.

Into the unstructured.
No GPS. No map.

A pitch-black basement, with no GPS and no prior map of the space. Lit only by its own light, our drone perceived every obstacle around it in real time.

Our drone flying through a dark basement, lit only by its own light — Flight test in a dark basement, lit only by the drone's own light.

One perception stack for every environment.

A full-stack platform that combines advanced radio sensing, AI-driven perception and autonomy software, so systems operate reliably in complex, real-world environments where traditional sensors fail.

Hardware

Sub-terahertz radar

High-resolution sub-THz radar that operates in smoke, fog, dust and darkness, where cameras and LiDAR fail.

It delivers high range and spatial resolution in a compact, low-SWaP-C package built for industrial, robotics and defense environments.

Software

Open Loop Autonomy

Our autonomy framework navigates, tracks and completes missions using onboard sensing and compute alone: no GPS, no pre-built maps, no continuous communication.

Because it is observation-centric, it keeps working in GPS-denied and contested environments and holds the mission through communication loss. It reduces the need for constant human control while still supporting supervision, and deploys as a software upgrade to existing systems.

Machine learning

Sensor fusion and perception

Machine learning turns raw radar and camera data into a real-time understanding of the environment, more robust than any single sensor alone.

Edge-optimized models run object detection and tracking with low latency, and keep perception accurate when visibility drops.

Deployment

Built to integrate

The platform runs in real time on edge devices such as NVIDIA Jetson and integrates with existing robotics and autonomy stacks.

Deploy it as a module or a full system, add capability through software upgrades to Open Loop Autonomy, and scale from a pilot to a fleet-wide rollout.

In the field

From perception to safe flight.

A real flight, replayed from the drone's own log. It spots the obstacles around it, marks a safety zone around each one, and keeps planning a clear path through.

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