A closed-loop sim-to-real RL policy trained in a simplified frictionless simulator achieves sub-millimeter microfiber shape control on physical hardware via visual feedback without retraining.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
The Method of Matched Sections reconstructs fair thin-plate-spline surfaces by decomposing the domain into boundary-matched 1D components that automatically enforce continuity of second- and third-order derivatives.
citing papers explorer
-
Closed-Loop Sim-to-Real Reinforcement Learning for Deformable Microfiber Shape Control
A closed-loop sim-to-real RL policy trained in a simplified frictionless simulator achieves sub-millimeter microfiber shape control on physical hardware via visual feedback without retraining.
-
Thin Plate Spline Surface Reconstruction via the Method of Matched Sections
The Method of Matched Sections reconstructs fair thin-plate-spline surfaces by decomposing the domain into boundary-matched 1D components that automatically enforce continuity of second- and third-order derivatives.