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

Updated September 26, 2026 · 2:44 PM · 4 · source date March 5, 2026

Summary

Bringing Robotics AI to Embedded Platforms: Dataset Recording, VLA Fine‑Tuning, and On‑Device Optimizations Bringing Robotics AI to Embedded Platforms: Dataset Recording, VLA Fine‑Tuning, and On‑Device Optimizations Enterprise Article Published March 5, 2026 Upvote 19 Gaetan Bahl gbahlnxp nxp Enzo Ruedas eruedas nxp Tess Boivin tboivin nxp 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 .

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Key facts
  • Bringing Robotics AI to Embedded Platforms: Dataset Recording, VLA Fine‑Tuning, and On‑Device Optimizations Enterprise Article Published March 5, 2026 Upvote 19 Gaetan Bahl gbahlnxp nxp Enzo Ruedas eruedas nxp Tess Boivin tboivin nxp 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 .
  • Deploying these models on embedded robotic platforms remains a challenge due to tight constraints in terms of compute, memory, and power, as well as real-time control requirements.
  • In synchronous control pipelines, while the VLA is running inference, the arm is idle awaiting commands leading to oscillatory behavior and delayed corrections.
  • To tackle that, asynchronous Inference can enable smooth and continuous motion by dissociating generation from execution.
  • However, to be effective, the end-to-end inference latency must remain shorter than the action execution duration .
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