Dermot O’Driscoll leads the go-to-market organization for Arm’s Physical AI business, helping partners innovate and scale Arm-based solutions across automotive, robotics, and autonomous systems.
With more than 20 years at Arm, Dermot has held a range of engineering and leadership roles spanning IT, EDA, CPU and SoC design. His deep technical background and commercial experience help bridge technology and ecosystem adoption across emerging Physical AI markets.
Dermot received his bachelor’s degree in Electronics Engineering and a master’s degree in Microelectronics from the University of Edinburgh, Scotland.
Recent advances in foundation models, vision-language-action systems, and edge AI are expanding what robots can sense, reason about, and do. The next challenge is turning those capabilities into autonomous systems people can trust in the real world, whether restoring mobility through intelligent prosthetics, delivering critical medical supplies, or helping workers operate more safely and productively alongside intelligent machines. As robots become more capable, engineering priorities increasingly shift toward system-level design. Success will depend on how intelligence is distributed across the robot, how learned and deterministic behaviors interact and how compute platforms balance real-time responsiveness, efficiency, and safety. Drawing on lessons from across the robotics ecosystem, this session will examine key compute principles behind trusted physical AI, including how intelligence is distributed across cloud and edge compute, the coordination of learned and deterministic behaviors, and how developers can design systems that remain adaptable, reliable, and safe as they scale to production. Attendees will leave with a practical framework for building trusted physical AI systems that extend human capability while meeting the real-world demands of safety, efficiency, and scale.
