Kai Liu

EvoHIL achieves unprecedented improvements in robotic manipulation by dynamically adapting reward models and ensuring action coherence, setting a new standard for human-in-the-loop learning.

WAM-TTT allows robot models to adapt to new tasks using only raw human videos, eliminating the need for additional demonstrations or fine-tuning.

Imagine training robots to manipulate objects in the real world, but entirely within a high-fidelity, diffusion-based dream.