Motion Generation for Food Topping Challenge 2024: Serving Salmon Roe Bowl and Picking Fried Chicken

FULL PAPER Motion Generation for Food Topping Challenge 2024: Serving Salmon Roe Bowl and Picking Fried Chicken Koki Inami a, Masashi Konosu a, Koki Yamane a, Nozomu Masuya a, Yunhan Li a, Yu-Han Shu a, Hiroshi Sato a, Shinnosuke Homma a, and Sho Sakaino b a Intelligent and Mechanical Interaction Systems, Degree Programs in Systems and Information and Engineering, Graduate School of Science and Technology, University of Tsukuba, Japan; b Department of Intelligent Interaction Technologies, Institute of Systems and Information Engineering, University of Tsukuba, Japan; ARTICLE HISTORY Compiled May 1, 2025 ABSTRACT Although robots have been introduced in many industries, food production robots are yet to be widely employed because the food industry requires not only delicate movements to handle food but also complex movements that adapt to the environment. Force control is important for handling delicate objects such as food. In addition, achieving complex movements is possible by making robot motions based on human teachings. Four-channel bilateral control is proposed, which enables the simultaneous teaching of position and force information. Moreover, methods have been developed to reproduce motions obtained through human teachings and generate adaptive motions using learning. We demonstrated the effectiveness of these methods for food handling tasks in the Food Topping Challenge at the 2024 IEEE International Conference on Robotics and Automation (ICRA 2024). For the task of serving salmon roe on rice, we achieved the best performance because of the high reproducibility and quick motion of the proposed method. Further, for the task of picking fried chicken, we successfully picked the most pieces of fried chicken among all participating teams. This paper describes the implementation and performance of these methods. KEYWORDS Bilateral control; motion-copying system; imitation learning; 1. Introduction Robot automation has progressed in recent years, especially in the manufacturing industry because robots excel at precise repetitive movements in a well-equipped environment. However, their use in the food industry has been limited because of the complexity of the work and the need to generate movements that adapt to the work environment.