Twincher: Bijective Representation Learning for Robust Inversion of Continuous Systems
Perception, state estimation, and action planning in physical systems often reduce to inverting a continuous forward map $p \mapsto y = f(p)$ (a renderer, a physics simulator, or a learned surrogate) that can be queried but not inverted in closed form. Learned direct inverses are limited by an approximation error that decreases only gradually with the amount of data, whereas iterative solvers that minimize the misfit in observation space can stall at spurious stationary points. We study an intermediate route: learning a representation $r(y)$ in which the composite map $p \mapsto r(f(p))$ is a well-conditioned bijection and which is insensitive to perturbations of $y$ normal to the data ma...
| Authors | Arkady Gonoskov |
|---|---|
| Primary category | cs.LG |
| Published | 13 May 2026 |
| Last updated at source | 2 October 2026 |
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