Fine Tuning OpenVLA Model

Robotics has had its own foundation-model moment. Vision-language-action models take a camera feed and a plain-language instruction — "pick up the red block and put it in the bin" — and output robot actions directly, without the hand-written perception and control stack that used to sit in between. OpenVLA is the leading open model of this kind, and because it is open you can fine-tune it on your own robot, your own gripper and your own tasks rather than accepting whatever it learned in the lab.

In this hands-on 1-day course, you will take OpenVLA end to end: understanding how a VLA model turns pixels and language into actions, collecting and formatting demonstration data for your own setup, and running efficient LoRA fine-tunes that train on a single GPU. You will then evaluate the result properly — success rates on held-out tasks, failure-mode analysis, and the sim-to-real gap — before deploying the tuned policy onto hardware with the safety limits any real robot needs.

You will leave with a fine-tuned VLA policy, a reusable data-collection and training pipeline, and a realistic sense of what these models can and cannot do today. Ideal for robotics engineers, ML practitioners, researchers and technically-minded makers who want to move from scripted robot behaviour to learned, instruction-following control.