Abstract
Intelligent robots are required to fully understand human intentions and operations in order to support or collaborate with humans to complete complicated tasks, which is typically implemented by employing human-machine interaction techniques. This paper proposes a new robotic learning framework to perform numeral writing tasks by investigating human-machine interactions with human preferences. In particular, the framework implements a trajectory generative module using a generative adversarial network (GAN)-based method and develops a human preference feedback system to enable the robot to learn human preferences. In addition, a convolutional neural network, acting as a discriminative network, classifies numeral images to support the development of the basic numeral writing ability, and another convolutional neural network, acting as a human preference network, learns a human user’s aesthetic preference by taking the feedback on two written numerical images during the training process. The experimental results show that the written numerals based on the preferences of ten users were different from those of the training data set and that the writing models with the preferences from different users generate numerals in different styles, as evidenced by the Frechet inception distance (FID) scores. The FID scores of the proposed framework with a preference network were noticeably greater than those of the framework without a preference network. This phenomenon indicates that the human-machine interactions effectively guided the robotic system to learn different writing styles. These results prove that the proposed approach is able to enable the calligraphy robot to successfully write numerals in accordance with the preferences of a human user.
Highlights
Intelligent machines, including robots, autonomous vehicles, and assistance systems, have been widely applied in our daily lives to support various activities, such as communication, business, transportation, and healthcare
The main contributions of this work are twofold: 1) a robot learning framework based on both human-robot interactions and the feedback of robotic writing results; and 2) an efficient generative adversarial network (GAN) training approach for trajectory generative module optimization
PRELIMINARY Deep reinforcement learning from human preferences as reported in [26] is able to train a preference network to reflect a human’s preference, where the preference feedback is used as the reward function for a reinforcement learning (RL) system
Summary
Intelligent machines, including robots, autonomous vehicles, and assistance systems, have been widely applied in our daily lives to support various activities, such as communication, business, transportation, and healthcare. This paper proposes a learning framework to enable calligraphy robots to learn to write numerals with human preferences by further developing the work of [26] to address the above two challenges. The proposed learning framework allows the robot to develop a high-performance writing skill consistent with human preferences. The main contributions of this work are twofold: 1) a robot learning framework based on both human-robot interactions and the feedback of robotic writing results; and 2) an efficient GAN training approach for trajectory generative module optimization. The goal of the previous work, i.e., the DE- and GAN-based calligraphy systems, was to find trajectory generative models for the Chinese strokes; in contrast, this work aims to enable the robot to learn the writing preferences of humans by using HMI.
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