Enabling Physical AI: The Role of Simulation Technology in Bridging the Physical and Digital Worlds

The coming decade is expected to be transformational, driven by the emergence of Physical AI as a key enabler of autonomous systems operating in the real world. The anticipated advancements will potentially boost capabilities of machines to see, understand, reason, and execute complex actions in the physical world. However, training such systems remains challenging: collecting real world data through teleoperation and demonstrations is costly, time consuming, and difficult to scale. As a result, simulation based synthetic data generation has become essential. However, this approach introduces a sim-to-real gap, i.e., a discrepancy between the real and the virtual world, which is particularly large for simplified models.

This presentation examines the crucial role of advanced simulation technology in unlocking the full potential of Physical AI. We highlight in detail how highly realistic, physics-based, accurate, robust and at the same time ultra-fast models – usually referred to as Digital Twins – can help to close or minimize the sim-to-real gap. The presented concepts outline a pathway toward reliable, industrial grade Physical AI, opening new opportunities for autonomous systems in industrial applications.