Most vision-language-action (VLA) demos stop at “the robot moved.” Real applications need a robot that can determine when a task did not work and recover from the failure. In this two-hour, instructor-led session, we build that application live on a low-cost SO-101 arm using Intel’s open-source Physical AI Studio and OpenVINO.
We record demonstrations, fine-tune a π0.5 VLA policy to follow plain-language instructions for a simple pick-and-place task, deploy it, and then break it on purpose. Next, we add an independent vision-language model that judges whether each step succeeded, along with deterministic application logic that resets and retries the task. Then, we introduce the same failure again.
Along the way, we map each workload to the silicon best suited to it: the policy runs on the GPU, oversight runs on the NPU, and control and safety run on the CPU. Attendees leave with the architecture and decision logic needed to build robust VLA applications on their own hardware. The complete application also runs on a Trossen WidowX AI featured at the Intel booth.