Title: Full Workspace Generation of Serial-link Manipulators by Deep Learning based Jacobian Estimation,
Abstract: Apart from solving complicated problems that require a certain level of
intelligence, fine-tuned deep neural networks can also create fast algorithms
for slow, numerical tasks. In this paper, we introduce an improved version of
[1]'s work, a fast, deep-learning framework capable of generating the full
workspace of serial-link manipulators. The architecture consists of two neural
networks: an estimation net that approximates the manipulator Jacobian, and a
confidence net that measures the confidence of the approximation. We also
introduce M3 (Manipulability Maps of Manipulators), a MATLAB robotics library
based on [2](RTB), the datasets generated by which are used by this work.
Results have shown that not only are the neural networks significantly faster
than numerical inverse kinematics, it also offers superior accuracy when
compared to other machine learning alternatives. Implementations of the
algorithm (based on Keras[3]), including benchmark evaluation script, are
available at this https URL . The M3
Library APIs and datasets are also available at
this https URL .