Thomas Cohn

This work extends planning under a Riemannian metric from unconstrained spaces to constraint submanifolds by applying the induced metric in both a sampling-based planner and a trajectory optimizer.

Suboptimal data can be a goldmine for robotic learning, and Ambient Diffusion Policy unlocks its potential by filtering out the noise that typically hampers performance.