TensorRT Optimization for Robotics: Accuracy, Latency, and Throughput

#architecture #machinelearning #performance #robotics

TensorRT Optimization for Robotics: Accuracy, Latency, and Throughput

Focus: NVIDIA TensorRT and edge inference\
Audience: Flutter, Android, edge AI, and robotics engineers

The problem

Converting a model to an optimized runtime can improve inference speed,
but precision changes, dynamic shapes, unsupported operators, and
preprocessing mismatches can degrade accuracy or deployment reliability.

What this guide covers

This tutorial focuses on practical architecture and performance
engineering for production systems. The central principle is to measure
the complete pipeline, keep latency-sensitive work close to the
hardware, and make failure states visible to the operator.

Reference architecture

Training/export model → ONNX validation → TensorRT engine build →
correctness suite → latency/throughput benchmark → versioned Jetson
deployment.

Implementation plan

Freeze the preprocessing contract and establish a baseline with the original framework.
Validate exported ONNX outputs against the source model on representative inputs.
Benchmark FP32/FP16/INT8 only when supported; for quantization, calibrate and test task-level metrics.
Measure cold engine build separately from steady-state inference and include preprocessing/postprocessing.
Version the engine with model hash, TensorRT/CUDA versions, input shapes, precision, and target hardware.

Example

# Example benchmark dimensions to record.\ninput_shape: [1, 3, 640, 640]\nprecision: fp16\nwarmup_iterations: 100\nmeasured_iterations: 1000\nmetrics: [p50_ms, p95_ms, throughput_fps, peak_memory_mb]

The snippets are illustrative starting points, not drop-in integrations.
Match APIs and version requirements to the exact SDK, OS image, hardware
revision, and runtime in your project.

Performance checklist

Measure end-to-end latency, not only one function or model invocation.
Report p50 and p95/p99 latency alongside throughput, memory use, and dropped work.
Bound queues and define what happens when consumers fall behind.
Avoid repeated frame copies and expensive work on Flutter's UI isolate.
Test sustained workloads after the device reaches thermal equilibrium.
Keep UI state, device state, and robot state distinct.
Add timeouts, cancellation, reconnection, and explicit stale-data indicators.
Profile on the actual target hardware using a reproducible build.

Common pitfalls

Do not assume an engine built on one GPU architecture is portable to every target.
Do not report only the fastest single run.
Do not accept a speedup without validating model quality and downstream robot behavior.