Ashutosh Saxena on the Graph Physical AI Approach: Bridging Physics and Data for Scalable Robotics

Attendance Link: https://tinyurl.com/robotsem-fall-2025

Autonomous robots still struggle in the field — when tasks drift from the script, materials deform, weather turns unpredictable, or unmodeled interactions arise. Across mobile robots, manipulators, and humanoids, such edge cases reveal the persistent gap between lab-trained AI and real-world reliability. While vision–language–action (VLA) models show promise in unifying perception, reasoning, and control, their reliance on massive datasets and retraining makes them fragile in dynamic, data-scarce settings. Robots cannot scale by data — they must scale by understanding. I will present Graph Physical AI (G-PAI), a foundation model that embeds physics–neural operators directly into its multimodal design, enabling data-efficient learning and robust adaptation across robot types. Its compositional architecture links perception, planning, and control agents through a shared physics-informed core, ensuring interpretability and fast generalization. G-PAI is already powering robots in demanding conditions — from warehouses and construction sites to agricultural operations. It has formal safety benchmarks on long-tail edge cases with an OEM, with broader testing underway across additional domains. Together, these results mark a practical step toward deployable, general-purpose Physical AI.

Bio: Dr. Saxena received his Ph.D. in Artificial Intelligence with Andrew Ng from Stanford University. He went on to become an accomplished professor at Cornell University. He co-founded Katapult (NSDQ: KPLT), Brain of Things ($8 million ARR), and Caspar AI (Top 100 AI Companies).

Dr. Saxena's expertise is in Agentic AI for Autonomous Systems like robots, cars and healthcare. He has published about a hundred papers in top venues like NeurIPS, ICML & CVPR, with numerous best paper awards and 20,000+ citations. His work on Large-scale models for robotics was awarded the 10-year test of time award in 2023.

Please visit https://stanfordasl.github.io/robotics_seminar/ for this quarter’s lineup of speakers. Although we encourage live in-person attendance, recordings of talks will be posted also.

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