Karol Hausman

Karol Hausman is a professor, researcher and technology executive known for his work on robot learning and artificial intelligence.

Research career Hausman's work is focused on creating an AI that can power any robot. He has been serving as an adjunct professor at Stanford University since 2021. He researched perception–action loops and interactive perception, showing how robots can actively move to reduce uncertainty in object pose estimation by coupling probabilistic state estimation with control.

In Scaling Simulation-to-Real Transfer by Learning a Latent Space of Robot Skills, published in The International Journal of Robotics Research (2020), Hausman and collaborators proposed learning a low-dimensional latent space of skills in simulation and adapting them efficiently to physical robots. The method improved transfer efficiency and reduced the amount of real-world data required for locomotion and manipulation tasks.

At Google Brain and Google DeepMind, he worked as a senior researcher on large-scale robotic learning systems that trained generalizable grasping and manipulation policies from extensive robot interaction data. His work emphasizes shared representations and scalable learning architectures for general-purpose robotic intelligence.

He served as an chair/associate editor of the International Conference on Robotics and Automation, International Conference on Learning Representations, Neural Information Processing Systems, International Conference on Machine Learning.

Physical Intelligence Hausman co-founded Physical Intelligence, a technology company that aims to develop foundation models and learning algorithms for physically embodied AI systems.