Unifying Deep Predicate Invention with Pre-trained Foundation Models
UniPred learns concepts from human demonstrations with an agent in the loop, connecting perceptual learning to long-horizon robot planning.
Robin王乾玮
I am a first-year Ph.D. student in Electrical & Computer Engineering at Princeton University, advised by Tom Silver in the Princeton Robot Planning & Learning Lab (PRPL). I am supported by the Gordon Wu Fellowship.
Previously, I received my B.S. in Computer Science with Honors from the University of Michigan, Ann Arbor.
I study contact-rich manipulation: contact with the world through physical interaction with objects and everyday environments, and contact with humans through physical interaction and collaboration between people and robots.
UniPred learns concepts from human demonstrations with an agent in the loop, connecting perceptual learning to long-horizon robot planning.
Coding agents synthesize reusable policies for generalized task and motion planning across 28 simulated environments.
Zero-shot spatial reasoning through agent-generated Python programs and 3D visual tools.
Open-vocabulary 3D scene graphs built directly from point clouds for robot navigation.
A shared-control wheelchair that combines user input with autonomous assistance for navigation in built environments.
A little life outside the lab.
Always happy to talk research, exchange ideas, or help where I can. Get in touch

