Bringing Robotics AI to Embedded Platforms: Dataset Recording, VLA Fine‑Tuning, and On‑Device Optimizations
Hugging Face
NXP demonstrated how to deploy Vision-Language-Action (VLA) models on embedded robotic platforms by combining dataset recording practices, fine-tuning strategies, and hardware optimization. The team achieved 0.32-second inference latency on the i.MX 95 SoC for the ACT model (down from 2.86 seconds unoptimized) while maintaining 89% accuracy on a tea-bagging task across 30 test episodes. The approach decomposes VLA inference into separate vision, language, and action stages, applies selective quantization, and uses asynchronous scheduling to keep inference time below the action execution duration.