A new arXiv preprint presents a finite-difference bowed-string simulation that resolves Stribeck friction implicitly, alongside a diagnostic for playing regimes and Schelleng bow-force limits computed for four strings. It also revisits a minimum-bow-force law and compares a recurrent learned controller against a supervision ceiling that constrains how much imitation can help.
A new arXiv preprint proposes a diffusion-based multiple-shooting method for indirect optimal control, aimed at generating fuel-efficient spacecraft trajectories. The authors argue that prior diffusion-based control work has largely neglected optimality guarantees, which their approach seeks to address. The work sits at the intersection of generative modeling, robotics-style control, and aerospace trajectory design.
A new arXiv paper surveys how diffusion-based learning methods can be applied to robot learning, framing the problem through ergodic control and controlled diffusion. It is written as a combined review and tutorial, intended to give researchers a structured entry point to the underlying statistical machinery. The work is cross-listed in machine learning and focuses on deriving complex distributions from data for control tasks.