Physical AI

Chairpersons’ welcome and opening address

How physical AI is moving from experimentation into real-world industrial deployment, and what it takes to scale autonomous systems with the governance, trust and operational discipline required in complex industries like aerospace

A discussion on how open-source robotics is enabling faster development, broader experimentation and more scalable deployment of Physical AI, and why collaboration is becoming essential for enterprise adoption.

As robots become more capable of recognising emotion, responding to human behaviour and operating alongside us in increasingly personal environments, an important question emerges: can a machine truly care – and does it matter if we believe that it does?

This fireside explores the evolving relationship between humans and socially intelligent robots, from companionship and care to workplaces and public spaces. We’ll examine how embodiment changes our relationship with AI, what makes people trust and form connections with machines, and where the boundaries should lie as robots become more present in our everyday lives.

The discussion will consider whether empathy can be designed, the difference between simulating care and experiencing it, and how we can ensure the next generation of Physical AI is built around genuine human needs rather than technological capability alone.

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

As autonomous systems move from isolated pilots to large-scale deployment, the challenge shifts from making individual machines intelligent to coordinating entire fleets, workflows and environments. Scaling Physical AI requires an orchestration layer capable of connecting robots, AI models, enterprise systems and human operators into a unified operational ecosystem.

This session explores what it takes to build the operating system for autonomy at scale – from fleet orchestration and dynamic task allocation to real-time decision-making, interoperability and continuous optimisation. Discover how organisations can move beyond standalone robotics deployments towards intelligent, coordinated operations where physical and digital systems work together seamlessly.

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

This session examines how AI is transforming intralogistics, manufacturing and warehouse automation through autonomous item picking, self-organizing robotic fleets, adaptive material flow systems, and agile execution platforms. Examples will demonstrate how AI is enabling robots not only to follow instructions but to dynamically coordinate tasks, react to changing conditions, and continuously optimize throughput.

The presentation will showcase the next step in the evolution of automation—from orchestrating intelligent systems to enabling autonomous execution across entire logistics and production ecosystems.

• Physical AI and the evolution from digital intelligence to real-world action
• Autonomous item picking powered by AI and machine vision
• Self-organizing robotics and adaptive material flow systems
• AI-enabled management platforms for real-time operational optimization
• The convergence of software, robotics and AI into autonomous industrial ecosystems

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)