Max Robotics — Global Service Robot Directory + US Certification

About

Samuel C. Stanton is a pioneering researcher at the intersection of physics-informed machine learning, robotics, and autonomous scientific discovery. His work fundamentally rethinks how artificial intelligence can model and interact with the physical world. Stanton’s most impactful contribution is his deep analysis of inductive biases in Hamiltonian Neural Networks (2022, 14 citations), where he deconstructs why these physics-inspired models outperform standard neural networks for learning dynamics—while also exposing their limitations for non-conservative systems. This work provides crucial guidance for applying AI to real-world physics problems. Stanton also advances Gaussian processes for online decision-making (2021, 5 citations), developing efficient variational methods that make principled uncertainty quantification practical for autonomous systems. His forward-looking vision extends to reimagining robotics as an integrated materials-energy-control problem (2018) and proposing situated experimental agents for scientific discovery (2018), where robotic collaborators actively participate in the research process. Through these contributions, Stanton is shaping a future where AI systems not only learn from physics but actively participate in scientific exploration.