Perception and planning in physical systems often require inverting a continuous forward map $p \mapsto y = f(p)$, e.g., a renderer or a simulator, that can be queried but not inverted in closed form. Learned inverses are limited by their approximation error, whereas iterative solvers in observation space can stall at spurious stationary points. We instead learn a representation $r(y)$, insensitive to perturbations of $y$ normal to the data manifold, in which $p \mapsto r(f(p))$ is a well-conditioned bijection. In these coordinates, the least-squares inverse problem has no spurious interior stationary points, so that Gauss-Newton iteration with the exact forward model converges to the accuracy the data permit; the learned model need only be qualitatively correct. We introduce Twincher, an exactly invertible architecture of localized area-preserving rotations, trained with Jacobian-level objectives and a domain-growing curriculum. On synthetic maps with tunable nonconvexity, the worst-case residual reaches machine precision within five iterations once the query budget suffices, whereas an MLP-based baseline improves only gradually. In 6D localization of industrial objects in real depth images of the MVTec ITODD benchmark, given instance masks, twinchers trained only on CAD renderings make the iteration converge from a fixed initial guess within a few steps, without feature matching, pose regression, or pose hypotheses; in observation space, the same iteration fails for up to half of the views. All validation poses pass the strictest BOP thresholds and fit the sensor data about as closely as the ground-truth poses; 80\% of all 239 instances (67\% with visible masks only) are solved in agreement with the sensor data, and the number of training iterations grew only 1.5-fold from two to four pose parameters. Code is open source.

翻译：暂无翻译