Andrew F. Luo

A function-preserving Real-to-Sim-to-Real framework that generates synthetic demonstrations from reconstructed assets without teleoperated source trajectories is proposed, enabling robust generalization across unseen object geometries and diverse visual conditions in contact-rich and long-horizon tasks.

GenHOI allows humanoid robots to learn complex object interactions from a single video, eliminating the need for extensive training or demonstrations.