Robot learns Chinese calligraphy from Demonstrations

Sun, Yuhao; Qian, Huihuan; Xu, Yangsheng

doi:10.1109/iros.2014.6943186

Cited by 27 publications

(22 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…almost all existing research applied predefined font databases, which were created by computational programming [12], [13] or by follow-up mechanisms [14], [22]; on the following four important features: 1) font database implementation methods, 2) ability to write new characters, 3) ability to write various font styles, and 4) human-robot interactions.…”

Section: Optionsmentioning

confidence: 99%

“…Such robots work similar to printers or typewriters that repeatedly produce characters in exactly the same way as specified in the font libraries. Other scientists applied the follow-up ability of manipulators to obtain font information [14]. This method successfully imparts human calligraphic styles to robots, but it still entails much human work to enable manipulators to produce enough fonts.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Use of Automatic Chinese Character Decomposition and Human Gestures for Chinese Calligraphy Robots

Chao

Huang

Lin

et al. 2019

IEEE Trans. Human-Mach. Syst.

View full text Add to dashboard Cite

Conventional Chinese calligraphy robots often suffer from the limited sizes of predefined font databases, which prevent the robots from writing new characters. This paper presents a robotic handwriting system to address such limitations, which extracts Chinese characters from textbooks and uses a robot's manipulator to write the characters in a different style. The key technologies of the proposed approach include the following:(1) automatically decomposing Chinese characters into strokes using Harris corner detection technology and (2) matching the decomposed strokes to robotic writing trajectories learned from human gestures. Briefly, the system first decomposes a given Chinese character into a set of strokes and obtains the stroke trajectory writing ability by following the gestures performed by a human demonstrator. Then, it applies a stroke classification method that recognizes the decomposed strokes as robotic writing trajectories. Finally, the robot arm is driven to follow the trajectories and thus write the Chinese character. Seven common Chinese characters have been used in an experiment for system validation and evaluation. The experimental results demonstrate the power of the proposed system, given that the robot successfully wrote all the testing characters in the given Chinese calligraphic style.

show abstract

Section: Optionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Use of Automatic Chinese Character Decomposition and Human Gestures for Chinese Calligraphy Robots

Chao

Huang

Lin

et al. 2019

IEEE Trans. Human-Mach. Syst.

View full text Add to dashboard Cite

show abstract

“…Inspired by literature reports [46,47], the maximum of (φ) by minimizing the H(φ) can be obtained as follows:…”

Section: Learning Algorithmmentioning

confidence: 99%

A Robot Learning Method with Physiological Interface for Teleoperation Systems

Luo

Yang

et al. 2019

Applied Sciences

View full text Add to dashboard Cite

The human operator largely relies on the perception of remote environmental conditions to make timely and correct decisions in a prescribed task when the robot is teleoperated in a remote place. However, due to the unknown and dynamic working environments, the manipulator’s performance and efficiency of the human-robot interaction in the tasks may degrade significantly. In this study, a novel method of human-centric interaction, through a physiological interface was presented to capture the information details of the remote operation environments. Simultaneously, in order to relieve workload of the human operator and to improve efficiency of the teleoperation system, an updated regression method was proposed to build up a nonlinear model of demonstration for the prescribed task. Considering that the demonstration data were of various lengths, dynamic time warping algorithm was employed first to synchronize the data over time before proceeding with other steps. The novelty of this method lies in the fact that both the task-specific information and the muscle parameters from the human operator have been taken into account in a single task; therefore, a more natural and safer interaction between the human and the robot could be achieved. The feasibility of the proposed method was demonstrated by experimental results.

show abstract

“…The main focus of research for robotic writing is the trajectory generation methods for robots to write characters or letters using a pen or ink brush [1,2,3,4,5], but this is not necessarily the case for Chinese calligraphy robots.…”

Section: Introductionmentioning

confidence: 99%

A data-driven robotic Chinese calligraphy system using convolutional auto-encoder and differential evolution

Gao

Zhou

Chao

et al. 2019

Knowledge-Based Systems

View full text Add to dashboard Cite

The Chinese stroke evaluation and generation systems required in an autonomous calligraphy robot play a crucial role in producing high-quality writing results with good diversity. These systems often suffer from inefficiency and non-optima despite of intensive research effort investment by the robotic community. This paper proposes a new learning system to allow a robot to automatically learn to write Chinese calligraphy effectively. In the proposed system, the writing qual

show abstract

Robot learns Chinese calligraphy from Demonstrations

Cited by 27 publications

References 10 publications

Use of Automatic Chinese Character Decomposition and Human Gestures for Chinese Calligraphy Robots

Use of Automatic Chinese Character Decomposition and Human Gestures for Chinese Calligraphy Robots

A Robot Learning Method with Physiological Interface for Teleoperation Systems

A data-driven robotic Chinese calligraphy system using convolutional auto-encoder and differential evolution

Contact Info

Product

Resources

About