Latency Constraint

The "train in cloud, deploy locally" model is critical for robotics.

The Fundamental Problem

Latency is the time delay between when a sensor detects something and when the robot can respond. In robotics, this delay can be the difference between graceful movement and catastrophic failure.

Risk of Cloud Control

Attempting to control a robot directly from the cloud introduces significant latency, making real-time balance and interaction impossible.

Latency Breakdown

Component	Typical Latency
Local sensor to edge compute	<1 ms
Edge inference (AI model)	5–20 ms
Local control loop	<1 ms
Total (Local)	<25 ms
Sensor to internet upload	10–50 ms
Internet to cloud	20–100 ms
Cloud inference	10–50 ms
Cloud to internet download	20–100 ms
Internet to robot	10–50 ms
Total (Cloud)	70–350 ms

Why This Matters

Consider a humanoid robot losing balance:

• Local control: Detects tilt in <1 ms, computes correction in 10 ms, applies torque in <1 ms = Recovers balance
• Cloud control: Detects tilt, waits 150 ms for cloud response = Falls over

Real-world robotics requires control loops running at 100+ Hz (every 10 ms), which is simply not achievable with cloud latency.

Examples of Latency-Critical Tasks

Balance Control

Bipedal robots require constant micro-adjustments to maintain stability. Even 50 ms of delay can result in falling.

Manipulation

Grasping objects requires real-time force feedback. Cloud latency would result in dropped or crushed objects.

Obstacle Avoidance

A robot moving at 1 m/s travels 35 cm during a 350 ms cloud round-trip—potentially straight into an obstacle.

Human-Robot Interaction

Responsive behavior requires immediate reactions to human movements and gestures.

The Correct Architecture

important

AI models and control logic should be trained in the cloud or on a local workstation, but they must be deployed onto the local Jetson device for low-latency execution.

Best Practice: Train in Cloud, Deploy Locally

1. Development Phase:

   • Collect training data in simulation
   • Train neural networks on powerful GPUs
   • Validate models in simulated environments

2. Deployment Phase:

   • Export trained models (TensorRT, ONNX)
   • Load models onto Jetson Orin
   • Run inference locally with <10 ms latency

3. Refinement Phase:

   • Collect real-world data
   • Retrain models
   • Redeploy to edge

Edge Computing Is Non-Negotiable

For real-time robotics, local edge inference is mandatory. Cloud compute is for training only — never control.