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Apart from solving complicated problems that require a certain level ofintelligence, fine-tuned deep neural networks can also create fast algorithmsfor slow, numerical tasks. In this paper, we introduce an improved version of[1]'s work, a fast, deep-learning framework capable of generating the fullworkspace of serial-link manipulators. The architecture consists of two neuralnetworks: an estimation net that approximates the manipulator Jacobian, and aconfidence net that measures the confidence of the approximation. We alsointroduce M3 (Manipulability Maps of Manipulators), a MATLAB robotics librarybased on [2](RTB), the datasets generated by which are used by this work.Results have shown that not only are the neural networks significantly fasterthan numerical inverse kinematics, it also offers superior accuracy whencompared to other machine learning alternatives. Implementations of thealgorithm (based on Keras[3]), including benchmark evaluation script, areavailable at https://github.com/liaopeiyuan/Jacobian-Estimation . The M3Library APIs and datasets are also available athttps://github.com/liaopeiyuan/M3 .

Full Workspace Generation of Serial-link Manipulators by Deep Learning based Jacobian Estimation