Sehoon Ha

Humans can directly control complex embodied agents using a handful of learned, composable skills discovered through self-play, bypassing the need for low-level action control.

Real-world robot policies can be steered to novel, out-of-distribution behaviors with just 150 preference labels, dramatically increasing object transport height.

Forget hand-crafted reward functions: this RL framework lets a bicycle robot learn complex stunts from just a spatial guideline and a few key poses.