Abstract:
Robotic writing, particularly in the realm of traditional Chinese calligraphy, poses unique challenges due to the intricate nature of stroke patterns and the high precisi...Show MoreMetadata
Abstract:
Robotic writing, particularly in the realm of traditional Chinese calligraphy, poses unique challenges due to the intricate nature of stroke patterns and the high precision required. This paper presents an innovative image-based imitation learning framework designed to address these challenges by enabling robots to acquire and generalize writing skills from static images. By transforming static images into dynamic formats and incorporating a novel deviation analysis method, the framework enhances the learning of stroke thickness and allows for skill generalization across different writing scenarios. The proposed framework’s effectiveness is demonstrated through extensive simulations and real-world experiments.
Published in: 2024 30th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)
Date of Conference: 03-05 October 2024
Date Added to IEEE Xplore: 12 November 2024
ISBN Information: