# Bringing Robotics AI to Embedded Platforms: Dataset Recording, VLA Fine-Tuning, and On-Device Optimizations

DevFeed: [Bringing Robotics AI to Embedded Platforms: Dataset Recording, VLA Fine-Tuning, and On-Device Optimizations](<https://devfeed.tech/articles/bringing-robotics-ai-to-embedded-platforms-dataset-recording-vla-fine-tuning-and-on-device-optimizations-7404.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/nxp/bringing-robotics-ai-to-embedded-platforms>)

Author: Gaetan Bahl; Enzo Ruedas; Tess Boivin

Published: 2026-03-05T14:16:49Z

Content type: tutorial

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [Robotics](<https://devfeed.tech/topics/robotics.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [multimodal](<https://devfeed.tech/topics/multimodal.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [systems](<https://devfeed.tech/topics/systems.md>), [AI Inference](<https://devfeed.tech/topics/ai-inference.md>), [SOC](<https://devfeed.tech/topics/soc.md>), [data](<https://devfeed.tech/topics/data.md>), [Hardware](<https://devfeed.tech/topics/hardware.md>), [smolvla](<https://devfeed.tech/topics/smolvla.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [best-practices](<https://devfeed.tech/tags/best-practices.md>), [compression](<https://devfeed.tech/tags/compression.md>), [compute](<https://devfeed.tech/tags/compute.md>), [data](<https://devfeed.tech/tags/data.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [embedded](<https://devfeed.tech/tags/embedded.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [inference](<https://devfeed.tech/tags/inference.md>), [latency](<https://devfeed.tech/tags/latency.md>), [models](<https://devfeed.tech/tags/models.md>), [on-device](<https://devfeed.tech/tags/on-device.md>), [performance](<https://devfeed.tech/tags/performance.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [robotics](<https://devfeed.tech/tags/robotics.md>), [smolvla](<https://devfeed.tech/tags/smolvla.md>), [soc](<https://devfeed.tech/tags/soc.md>), [systems-engineering](<https://devfeed.tech/tags/systems-engineering.md>)

## AI overview

A practical guide to deploying Vision-Language-Action models on embedded robotic platforms. It covers reliable dataset recording, fine-tuning ACT and SmolVLA policies, asynchronous inference, latency-aware scheduling, and real-time optimization on the NXP i.MX 95 SoC.

## Source excerpt

Bringing Robotics AI to Embedded Platforms: Dataset Recording, VLA Fine-Tuning, and On-Device Optimizations Authors: Enzo Ruedas, Tess Boivin Recent advances in Large Language Models have enabled the transition from text-only reasoning to multimodal systems. First, with the integration of visual perception in Vision-Language Models (VLMs), and more recently with the generation of robot actions in Vision-Language-Action (VLA) models.