Recent advancements in AI have sped up the evolution of versatile robot designs. Chess provides a standardized environment that allows for the evaluation of the influence of robot behaviors on human behavior. This article presents an open-source chess robot for humanrobot interaction (HRI) research, specifically focusing on verbal and non-verbal interactions. OpenChessRobot recognizes chess pieces using computer vision, executes moves, and interacts with the human player using voice and robotic gestures. We detail the software design, provide quantitative evaluations of the robot's efficacy and offer a guide for its reproducibility. Keywords: Artificial Intelligence, Chess, Human-robot Interaction, Open-source, Transfer Learning 1. Introduction Robots are becoming increasingly common across a variety of traditionally human-controlled domains. Examples range from automated mowers that maintain community lawns to robots in assembly lines and agricultural settings. Recent scientific advancements in AI have enabled new opportunities for intelligent sensing, reasoning, and acting by robots. In particular, the rapid development of large language models, such as ChatGPT, and vision-language models, have lowered the barrier of human-to-robot communication by being able to transform text and images into interpretable actions or vice versa. As technology advances, it is likely that robots will attain greater capabilities and will be able to tackle tasks previously within the exclusive realm of human expertise. This ongoing evolution may also lead to closer and more productive interactions between humans and robots. At the same time, integrating different AI-based robotic components remains a challenge, and the human-robot interaction (HRI) field lags in terms of endorsing reproducibility principles (Gunes et al., 2022). Encouraging transparent and reproducible research, therefore, remains an ongoing task. Furthermore, chess has played an important role in advancing the field of AI, starting with Claude Shannon's chess-playing algorithm (Shannon, 1950) to the success of IBM's Deep Blue (Campbell et al., 2002) and DeepMind's self-play learning algorithm (Silver et al., 2018). In this paper, we incorporate modern AI algorithms into the design of a chess-playing robot to be used for studying HRI. HRI research may benefit from a chess-based setup because the game of chess provides a controlled rule-based environment in which the impact of robots on human players can be precisely measured.

Sometimes the headline says it all: A chess-playing robot accidentally broke the finger of its seven-year-old opponent during an exhibition match in Moscow. The child apparently moved his piece too soon, and the robot grabbed his finger and squeezed it, resulting in a fracture. A video shows the robot grabbing the boy's finger and holding it for several seconds before a group of people come to free him. The boy competed the next day, finishing the tournament. His parents, however, have reportedly contacted the public prosecutor's office.