Robotics Lab
Edge AI
Running intelligence at the edge — for latency, privacy and continuous operation without the cloud in the loop.
Overview
Robots cannot wait on the cloud for safety-critical decisions. Edge AI is about squeezing perception, planning and monitoring onto real robot compute — reliably, and without the fragility that on-device AI is often infamous for.
Edge AI also matters for privacy: data that never leaves the device is data that never leaks.
What this covers
Model efficiency
Quantization, distillation and pruning without sacrificing safety.
Runtime discipline
Deterministic latency and memory bounds on constrained hardware.
OTA updates
Safe, auditable over-the-air model updates with rollback.
Privacy by design
Data processed where it is generated whenever possible.
Fault handling
Edge stacks degrade gracefully when models or sensors fail.
Observability
On-device telemetry surfaces drift, latency and failure signals.
How it works
- 1
Models are profiled against the target compute budget.
- 2
Efficiency techniques applied and re-validated on task benchmarks.
- 3
Deployment via governed OTA channels with rollback.
- 4
Telemetry monitored for drift and regression.
Use cases
Autonomy research
On-device policies for latency-critical control.
Closed-loop performance without cloud dependency.
FAQ
Frequently asked
- Yes — the runtime targets common robotics compute platforms.