  Control strategies for robotic needle steering in soft tissues must account
for complex interactions between the needle and the tissue to achieve accurate
needle tip positioning. Recent findings show faster robotic command rate can
improve the control stability in realistic scenarios. This study proposes the
use of Extreme Learning Machines to provide fast commands for robotic needle
steering. A synthetic dataset based on the inverse finite element simulation
control framework is used to train the model. Results show the model is capable
to infer commands 66% faster than the inverse simulation and reaches acceptable
precision even on previously unseen trajectories.
