Physical AI

Chairpersons welcome and opening remarks

A macro view of how physical AI systems are structured:

• Edge AI (STBs, sensors, smart cameras)
• Multimodal perception & sensor fusion
• Foundation models for robotics
• Orchestration layers
• Governance & safety

What is genuinely new (foundation models, simulation at scale, edge compute), and what still breaks in production.

Cities are dynamic physical systems with humans, vehicles, sensors, and robots all interdependent. This session positions SDVs and Smart City infrastructure as Physical AI domains with shared challenges – sensing, prediction, optimization, and safe actuation.

How large robot fleets are coordinated, monitored, and optimised using AI models, including congestion reduction, routing efficiency, and ops integration.

Vehicles aren’t “just cars” — they’re mobile AI platforms that perceive city infrastructure, interact with people and machines, and make real-time decisions. This session covers:

Perception stacks in mobility vs robotics

Predictive models for unstructured urban interaction

Sensor fusion between vehicles and infrastructure

Safety, simulation, and regulation parallels with robots

Join us as we take a deep dive into robotics – but as part of the bigger ecosystem.

• Robot foundation models
• Vision-language-action systems
• Generalisation challenges
• Build vs buy decisions
• Where humanoids fit (and don’t)

• Labour displacement vs augmentation
• Tele-ops & human-in-the-loop models
• Measuring ROI properly
• Enterprise adoption timelines
• What becomes autonomous first?

Physical AI in the enterprise: ROI, deployment patterns, and what to measure.