Learning Kinematics from direct Self-Observation using Nearest-Neighbour Methods

The document discusses a model-free approach to robotic kinematics, focusing on both inverse and forward kinematics. It emphasizes the use of techniques such as nearest-neighbor methods and artificial neural networks for data-driven learning in control of robots with unknown structures. The text highlights benefits and drawbacks of various methods while proposing analytical solutions for high accuracy in robotic movements.