Authors:
Yudai Yamamoto
1
;
Viktor Kozák
2
and
Ikuo Mizuuchi
3
Affiliations:
1
Department of Food and Energy Systems Science, Tokyo University of Agriculture and Technology, Naka-cho 2-24-16, Koganei-shi 184-0012, Tokyo-to, Japan
;
2
Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague, Jugoslávských Partyzánů 1580/3, 160 00 Praha 6, Czech Republic
;
3
Department of Mechanical Systems Engineering, Tokyo University of Agriculture and Technology, Naka-cho 2-24-16, Koganei-shi 184-0012, Tokyo-to, Japan
Keyword(s):
Medial Collateral Ligament, Lateral Collateral Ligament, Outside Pass, Strategies, Reinforcement Learning.
Abstract:
We study the leg motion for an outside pass in soccer, observing four different movement strategies. The aim of this research is to validate the presence of these four strategies by training an agent with a higher reward for kicking a faster ball. Additionally, we aim to explore the role of the collateral ligaments’ stiffness in the outside pass. We built two leg models: (a) a two-degree-of-freedom leg model that applies torque around the hip joint, and (b) a three-degree-freedom leg model that applies pitch-roll-yaw torque around the hip joint and pitch torque around the knee joint. We trained a Deep Deterministic Policy Gradient (DDPG) agent using these models and analyzed the torques around the hip and knee joints, as well as the ball velocity after the leg loses contact with the ball. We observed three strategies similar to human behavior throughout agent learning.