Yufeng Jin

Synthetic data generation via RL not only scales but also enhances generalization in bimanual dexterous manipulation by leveraging language-conditioned task annotations.

Automating the RL workflow with HARBOR cuts engineering costs and enhances policy transferability to real-world robots.

Standard LLMs can now perform complex bimanual robot manipulation tasks with impressive success rates, all without any task-specific training.

Robots can now achieve superior surface coverage with precise end-effector poses thanks to a new SE(3)-aware Stein Variational Gradient Descent method that outperforms existing trajectory optimization techniques.