Abstract
Human–robot interaction in board games is a rapidly developing field of robotics. This paper presents a robot capable of playing Russian checkers designed for entertaining, training, and research purposes. Its control program is based on a novel unsupervised self-learning algorithm inspired by AlphaZero and represents the first successful attempt of using this approach in the checkers game. The main engineering challenge in mechanics is to develop a board state acquisition system non-sensitive to lighting conditions, which is achieved by rejecting computer vision and utilizing magnetic sensors instead. An original robot face is designed to endow the robot an ability to express its attributed emotional state. Testing the robot at open-air multiday exhibitions shows the robustness of the design to difficult exploitation conditions and the high interest of visitors to the robot.
Highlights
Designing robots playing games with human opponents is a challenge
Russian checkers was chosen because this variant of the game is the most popular in Russia, and visitors at shows will be able to start the game without additional instructions
The general rules of Russian checkers are similar to other versions of this game, such as English checkers, except for a few points
Summary
Designing robots playing games with human opponents is a challenge. Among the earliest examples of playing robots dates back to the XVIII century when Wolfgang de Kempelen presented the first chess-playing robot called “The Turk” or “Automaton Chess Playing” [1,2]. Games that involve active interaction with physical media such as table tennis or pool require developing sophisticated automatic control algorithms rather than intelligent decision-making programs. Examples of such robots include a table tennis robot based on an industrial KUKA robot by J. Nierhoff et al [7] Other games such as chess and Go do not necessarily need a physical implementation of a robotic player, but designing the decision-making algorithms for such games is an extremely difficult task. The main challenges in the reported work were creating a novel playing algorithm, reliable hardware, and combining them in a machine, and the aim of our project is conducting a number of studies in human–machine interaction, especially in using robots for education.
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