ContactKernels: Adaptive Proposals using Geometry-COnditioned Wrenches for Contact-Rich Manipulation

Contact-rich insertion requires precise geometric alignment despite uncertainty in perception, geometry, and con- trol. When this uncertainty causes unintended contact, the re- sulting contact provides local information that can guide correc- tive motion. Sampling-based Model Predictive Control (SMPC) is well suited to this setting because it optimizes through forward simulation without requiring gradients through contact dynamics. However, standard SMPC methods typically use generic proposal distributions that do not exploit observed contact to determine where to search next. We introduce ContactKernels, a contact-adaptive proposal kernel that uses geometry-conditioned wrench measurements to estimate contact location and local contact geometry. The estimated contact state directly adapts the proposal distribution: contact location biases the search toward corrective motions, while the contact normal shapes the sampling covariance. We evaluate ContactKernels with Predictive Sampling (PS) and Model Predictive Path Integral (MPPI) control across multiple insertion geometries in simulation and on a real robot. In simulation, ContactKernels improves mean insertion success by over 50 % points across both controllers. On the real robot, it increases insertion success from 53.3 % to 90.0 % and reduces mean time to insertion by 33 %. These results show that contact information can improve sampling-based control not only as feedback, but also by directly determining where the controller searches