Interaction Understanding for Physical AI
Learning representations of meaningful relationships and state changes in the physical world
Mainstream perception systems identify objects or summarize entire scenes, but many real-world decisions depend on the relationships and state changes between them. This program studies interaction as the intermediate abstraction: grounded enough to reflect what is physically happening, yet structured enough to support prediction and action.
Current Direction
Learning interaction representations from largely unlabeled video and translating them into reliable, real-time perception models for new environments.