Abstract
Robotic calligraphy is a very challenging task for the robotic manipulators, which can sustain industrial manufacturing. The active mechanism of writing robots require a large sized training set including sequence information of the writing trajectory. However, manual labelling work on those training data may cause the time wasting for researchers. This paper proposes a machine calligraphy learning system using a Long Short-Term Memory (LSTM) network and a generative adversarial network (GAN), which enables the robots to learn and generate the sequences of Chinese character stroke (i.e., writing trajectory). In order to reduce the size of the training set, a generative adversarial architecture combining an LSTM network and a discrimination network is established for a robotic manipulator to learn the Chinese calligraphy regarding its strokes. In particular, this learning system converts Chinese character stroke image into the trajectory sequences in the absence of the stroke trajectory writing sequence information. Due to its powerful learning ability in handling motion sequences, the LSTM network is used to explore the trajectory point writing sequences. Each generation process of the generative adversarial architecture contains a number of loops of LSTM. In each loop, the robot continues to write by following a new trajectory point, which is generated by LSTM according to the previously written strokes. The written stroke in an image format is taken as input to the next loop of the LSTM network until the complete stroke is finally written. Then, the final output of the LSTM network is evaluated by the discriminative network. In addition, a policy gradient algorithm based on reinforcement learning is employed to aid the robot to find the best policy. The experimental results show that the proposed learning system can effectively produce a variety of high-quality Chinese stroke writing.
Highlights
Robot is becoming an important role in improving efficiency of recycling and sustaining industrial manufacturing [1]
To beat the above challenges, we introduce an Long Short-Term Memory (LSTM) network into a generative adversarial network (GAN)-based robotic calligraphy system [13], so as to implement an LSTM-based generative adversarial architecture
Since the robot participated in the generation process of the LSTM network, some unexpected errors still existed in the written results; the errors prevented the convolutional neural network (CNN) network from achieving the standard loss
Summary
Robot is becoming an important role in improving efficiency of recycling and sustaining industrial manufacturing [1]. Many learning-based approaches to robotic calligraphy have attempted to build automatic calligraphic robots These methods cannot generate the correct writing sequences for Chinese strokes. One is to manually pre-define the robot’s end joint angles for each writing action to write Chinese characters or letters [10,11] Such methods may require a lot of work from human engineers. In the field of robotics, Rahmatizadeh et al [12] tried to use GAN to transform an input image into a low-dimensional space and use LSTM to predict their robot’s each joint value All of these methods must require the massive training data to obtain action sequence information.
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