Abhishek Gupta

Learning robotic reward functions from a million trajectories reveals that comparing entire trajectories, not just individual frames, unlocks better generalization and learning from suboptimal data.

Robots can now navigate more reliably and across different bodies (wheeled vs. legged) thanks to a hierarchical model that separates high-level planning from low-level physical constraints.