Generating and Customizing Robotic Arm Trajectories using Neural Networks
Journal:
arXiv
Published Date:
Jun 25, 2025
Abstract
We introduce a neural network approach for generating and customizing the
trajectory of a robotic arm, that guarantees precision and repeatability. To
highlight the potential of this novel method, we describe the design and
implementation of the technique and show its application in an experimental
setting of cognitive robotics. In this scenario, the NICO robot was
characterized by the ability to point to specific points in space with precise
linear movements, increasing the predictability of the robotic action during
its interaction with humans. To achieve this goal, the neural network computes
the forward kinematics of the robot arm. By integrating it with a generator of
joint angles, another neural network was developed and trained on an artificial
dataset created from suitable start and end poses of the robotic arm. Through
the computation of angular velocities, the robot was characterized by its
ability to perform the movement, and the quality of its action was evaluated in
terms of shape and accuracy. Thanks to its broad applicability, our approach
successfully generates precise trajectories that could be customized in their
shape and adapted to different settings.
