LOInK: Learned Optimal Inverse Kinematics via Structured Neural Surrogate Models
Multibody
LOInK learns a bi-Lipschitz invertible mapping from configuration space to a decoupled task/latent space, structuring the latent space so that cost-minimizing solutions are placed at the origin, enabling efficient sampling of approximately optimal inverse kinematics solutions.
See LICENSE file Inverse KinematicsNeural Surrogate ModelRobotics