Infinite-Dimensional Closed-Loop Inverse Kinematics for Soft Robots via Neural Operators
This work formulates an infinite-dimensional closed-loop inverse kinematics (CLIK) scheme for soft robots by combining actuation-to-shape and shape-to-task mappings. It derives a Jacobian for this composite mapping using an infinite-dimensional chain rule, enabling the controller to reason about the entire robot shape directly during task solving. A neural operator network is used to learn the complex actuation-to-shape map.