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.

--Recent research has demonstrated the usefulness of imitation learning in autonomous robot operation. In particular, teaching using four-channel bilateral control, which can obtain position and force information, has been proven effective. However, control performance that can easily execute high-speed, complex tasks in one go has not yet been achieved. We propose a method called Motion ReT ouch, which retroactively modifies motion data obtained using four-channel bilateral control. The proposed method enables modification of not only position but also force information. This was achieved by the combination of multilateral control and motion-copying system. The proposed method was verified in experiments with a real robot, and the success rate of the test tube transfer task was improved, demonstrating the possibility of modification force information. I. INTRODUCTION In recent years, imitation learning [1] [2] [3], a learning-based approach that enables robots to imitate human behavior, has been attracting attention.

Because imitation learning relies on human demonstrations in hard-to-simulate settings, the inclusion of force control in this method has resulted in a shortage of training data, even with a simple change in speed. Although the field of data augmentation has addressed the lack of data, conventional methods of data augmentation for robot manipulation are limited to simulation-based methods or downsampling for position control. This paper proposes a novel method of data augmentation that is applicable to force control and preserves the advantages of real-world datasets. We applied teaching-playback at variable speeds as real-world data augmentation to increase both the quantity and quality of environmental reactions at variable speeds. An experiment was conducted on bilateral control-based imitation learning using a method of imitation learning equipped with position-force control. We evaluated the effect of real-world data augmentation on two tasks, pick-and-place and wiping, at variable speeds, each from two human demonstrations at fixed speed. The results showed a maximum 55% increase in success rate from a simple change in speed of real-world reactions and improved accuracy along the duration/frequency command by gathering environmental reactions at variable speeds.