Toan Nguyen

This work presents HOTICE, a whole-body humanoid learning framework for transporting objects through cluttered environments, and introduces Humanoid-Object Decoupled Potential Fields, which jointly encode collision-avoidance guidance for the robot and the carried object, enabling coordinated, obstacle-aware motion for both.

Sparse rewards are transformed into dense supervision, enabling VLA models to adapt robustly in real-time across diverse robotic tasks.

Robots get smarter at in-context learning by "thinking" visually about future trajectories, leading to better generalization and success rates in manipulation tasks.