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

Deploying Vision-Language-Action (VLA) models on embedded robotic platforms presents significant challenges due to constraints in compute, memory, and real-time control. This guide provides hands-on best practices for creating high-quality robotic datasets, emphasizing consistency, controlled environments, and the use of gripper-mounted cameras for improved manipulation. It details the process of fine-tuning VLA policies such as ACT and SmolVLA for specific tasks. The content also explores optimization strategies for deploying these models on hardware like the NXP i.MX95, using techniques like quantization and asynchronous inference to achieve real-time performance.