Yufei Wei

Robot policies can achieve faster task completion by learning to decouple *where* to go from *when* to execute, outperforming fixed-interval action prediction.

Transforming VLN-CE into a hierarchical framework allows agents to navigate unseen environments more efficiently, achieving state-of-the-art results.

Forget painstakingly tuning force magnitudes for sim-to-real transfer of contact-rich tasks; this work shows that learning just the *direction* of contact forces unlocks surprisingly robust performance.