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World Congress 2025 • Aug 20, 2025 • Session details

How Robots Learn to be Robots

Alexander Schwarz

Training autonomous robots is bottlenecked by costly real-world testing. What if they could master kinematics inside photorealistic digital twins first? Discover how generative AI bridges the sim-to-real gap.

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#1 about 2 min

Transitioning from digital AI agents to physical AI

Defining physical AI systems that interpret 3D environments and physical behaviors.

#2 about 2 min

Translating sensory inputs into physical robot actions

How robots process camera inputs and language instructions to coordinate physical movements.

#3 about 2 min

Bottlenecks in capturing real-world operational data

The scalability and safety limitations of physical teleoperation and real-world testing.

#4 about 2 min

A three-computer paradigm for robotics development

Structuring workflows around neural network training, synthetic data generation, and digital twin simulation.

#5 about 2 min

Managing heterogeneous AI policies across robot embodiments

Deploying specific foundation models for separate skills like perception, navigation, and manipulation.

#6 about 2 min

Accelerating reinforcement learning through parallel simulation

Training trial-and-error behaviors exponentially faster by running thousands of environments simultaneously on GPUs.

#7 about 1 min

Bootstrap datasets via imitation learning and teleoperation

Capturing expert human trajectories to establish baseline behaviors for complex tasks.

#8 about 2 min

Establishing standardized 3D workflows using OpenUSD

Leveraging the OpenUSD framework to build interoperable, non-destructive digital twins.

#9 about 3 min

Scaling human demonstrations with domain randomization algorithms

Applying perturbations to small sets of simulated teleoperation runs to multiply synthetic training trajectories.

#10 about 3 min

Categorizing world foundation models for physical AI

Introducing models optimized for future state prediction, style transfer, and spatial reasoning.

#11 about 2 min

Bridging the sim-to-real gap with multi-control networks

Converting coarse simulation segmentations into photorealistic video datasets to improve model generalization.

#12 about 3 min

Hallucinating valid trajectories using video diffusion models

Post-training prediction models with robot kinematics to generate new training scenarios without full physics rendering.

#13 about 3 min

Fusing internet video and synthetic telemetry for foundation models

Combining massive unstructured external data with targeted simulated outputs to pre-train highly adaptable control policies.

#14 about 2 min

Validating deployed robotic policies in digital factory environments

Performing large-scale software-in-the-loop testing within virtual industrial spaces before physical rollout.

Matching moments

2:22 min

Introduction to physical AI and the physical world

Moe Sani Moe Sani · WWC Europe 2026

2:58 min

Introduction to Omniverse and Isaac Sim platforms

Teresa Conceicao · WWC 2022

1:20 min

Shifting focus from large language models to physical AI

Sergio Perez Sergio Perez · WWC Europe 2026

2:23 min

Development roadmap for world models and physical robotics

Joerg Krall Joerg Krall · WWC Europe 2026

1:40 min

The three essential phases for physical AI development

Sergio Perez Sergio Perez · WWC Europe 2026

2:55 min

Moving from AI software buzzwords to building actual robots

Iulia Feroli Iulia Feroli · WWC Europe 2026

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