Edge hardware is moving fast. What's missing is the middleware that deploys models to devices, orchestrates fleets, streams telemetry, and pushes OTA updates without a site visit. That's where RoboticX lives.
Edge AI middleware for robotics
Billions are flowing into robotics hardware. But once the robot leaves the lab, the software story falls apart — proprietary C++ stacks with no observability, models that can't be updated without a technician on-site, and zero fleet-level intelligence.
The niche is narrow on purpose. We're building the middleware layer that makes their hardware useful at scale — and that's exactly the kind of infrastructure Orion knows how to ship.
Inference runtimes, fleet management, model delivery, and telemetry — the intelligence stack between the processor and the cloud.
Decisions happen on the device. The cloud is for learning, not for latency-sensitive control.
One robot is a project. A thousand robots running the same stack is a platform.
Each one maps directly to a gap we've seen in real-world robotics deployments.
A warehouse robot can't wait 200ms for the cloud to decide whether that's a person or a pallet. Quantized models running on Dragonwing, Jetson, and ARM silicon. The cloud trains; the device decides.
Updating a thousand robots shouldn't require a thousand site visits. Staged rollouts, canary testing across the fleet, automatic rollback if accuracy drops. CI/CD for physical machines.
Two hundred robots across twelve warehouses is an operations problem. Fleet health dashboards, remote diagnostics, task scheduling, geofencing, and anomaly detection — distributed-systems thinking applied to machines on wheels.
LiDAR, cameras, IMUs, proximity sensors — most of that data gets thrown away. We stream the useful signal back to the cloud for retraining and debugging. Vision models built for dust, bad lighting, and vibration.
A robot that doesn't know its own limits is a liability. Confidence-aware control: machines that slow down when perception degrades, ask for human input when a decision is ambiguous, and fail gracefully. Reliable autonomy within a well-defined envelope.
A long-horizon bet with a clear progression.
Benchmarking quantized models on Dragonwing and Jetson. Building telemetry schema and OTA delivery primitives. Internal testbed with AMR hardware.
End-to-end stack on real hardware in a controlled environment. One site, small fleet, every layer validated against physical reality.
Partner deployments across multiple facilities with real operators and real connectivity constraints. Fleet orchestration managing heterogeneous hardware across sites.
A productized middleware layer any robotics OEM can adopt. Plug in a board, connect to the RoboticX runtime, and get fleet management, model delivery, and telemetry out of the box.
If you're an OEM shipping robots that need smarter software, a warehouse operator tired of managing fleets with spreadsheets, or a researcher working on edge inference — we're looking for early partners who want to shape this stack alongside us.
Talk to the Programme →