A new workflow enables embedded systems to run advanced robotics AI by combining dataset recording, fine-tuning of vision-language-action (VLA) models, and on-device optimizations.
Researchers have demonstrated a pipeline that starts with collecting real-world robotic interaction data, then fine-tunes a pretrained VLA model to specialize in specific tasks, and finally applies optimizations like quantization and pruning to fit on resource-constrained hardware such as Raspberry Pi or Jetson Nano.
"This approach bridges the gap between large-scale AI models and practical deployment in low-power robots," said the team. The method reduces model size by 80% while retaining over 90% task accuracy.
Key steps include using a human teleoperation setup to gather diverse trajectories, converting them into multi-modal prompts, and leveraging LoRA for efficient fine-tuning. On-device inference employs TensorRT and OpenCV optimizations to achieve real-time performance.
This work paves the way for affordable, intelligent robots in homes and factories without relying on cloud connectivity.