Foundational Models

Name: Foundational Models
Start: 2026-09-18T00:30:00.000Z
End: 2026-09-18T03:30:00.000Z
Location: Pololu Robotics and Electronics

Pololu Robotics and Electronics, las-vegasThursday, Sep 17 · 8:30 PM

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Doors 8:30 PM

The Story

**LV Robotics: Foundation Models Are Changing How We Build Robots** # **About This Event** **For most of robotics history, intelligent automation was built forward: Task → Program → Robot → Action.** **Whenever a robot needed to perform a new task, engineers had to write new code, gather fresh demonstrations, retrain the architecture, and perform painstaking real-world tests. Today, that paradigm is shifting dramatically.** **A new generation of robot foundation models, Vision-Language-Action (VLA) models, and world models is decoupling physical intelligence from specific tasks—and even from specific hardware embodiments.** ## **Breakthroughs We’ll Explore** * **Google DeepMind’s Gemini Robotics 2: Leveraging unified models across diverse robot embodiments and rapidly adapting to unencountered physical platforms.** * **Stanford’s SimToolReal: Achieving zero-shot transfer of complex, dexterous tool manipulation directly from simulation to real-world environments.** * **Physical Intelligence’s π0.7: Demonstrating robust zero-shot cross-embodiment generalization and immediate performance on novel tasks.** * **OS3: Pretraining physical intelligence across 150,000 hours of video content spanning over 2,000 distinct tasks.** * **Moving Atoms: Building action-conditioned world models that enable robots to generate and learn from synthetic physical experience on GPUs.** * **Shiraz AI: Developing foundation models that translate human video demonstrations into actionable robot behaviors across embodiment gaps.** ## **From Forward to Reverse Inference** **We are moving away from traditional forward inference (Job → Training → Model → Action) toward reverse inference: evaluating a model's foundational physical capabilities to instantly infer what jobs a given robot can perform.** **This transition unlocks powerful possibilities:** * **Simulation becomes a primary source of experience.** * **Human video acts as scalable training data.** * **Skills become highly transfera

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