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While research-centric tools have lowered the entry barrier for robotics data collection, transitioning Vision-Language-Action models to production remains challenging due to fragmented edge deployment paths. This session presents a unified, PyTorch-native workflow spanning the full robotics lifecycle, from data capture and curation to optimized edge execution. We introduce a modular Physical AI pipeline designed to resolve the disconnect between research scripts and real-time hardware. The talk details practical patterns for robotics data capture and policy training in a unified PyTorch ecosystem, followed by concrete steps to export models via ExecuTorch. Using an OpenVINO backend, Quantizer, and AOT compilation, we address latency, accuracy, and operator coverage gaps, and demonstrate efficient on-device VLA inference. Using a WidowX pick-and-sort task as a case study, we demonstrate how to validate latency and numerical tolerances under physical constraints. Attendees will leave with a reference architecture and a checklist for monitoring, safety gates, and managing dataset drift, providing a roadmap for moving robotics VLA from research to production-grade edge deployment.
Dmitriy Pastushenkov is a passionate Software Product Manager at Intel with more than 20 years of comprehensive and international experience in the industrial automation, industrial Internet of Things (IIoT) and real-time operating systems and AI. Dmitriy has held various roles in... Read More →
Samet Akcay is a Principal AI Engineer at Intel who leads ML R&D efforts across Open Edge Platform libraries, including Intel Geti, Datumaro, Anomalib, Training Extensions, and Inference libraries. His research specializes self-supervised learning and multi-modal object detection... Read More →
