Robot Manifolds for Direct and Inverse Kinematics Solutions

We present a novel algorithm to estimate robot kinematic manifolds incrementally. We relate manifold learning with the forward and inverse kinematic of robots. The learned structure encodes this functions and so it is possible to recover them from the manifold. Our algorithm works without any knowledge of the robot kinematics and can potentially work in highly-dimensional spaces. We present some simulated examples to validate our approach.