Soccer kicking is a complex whole-body motion that requires intricate coordination of various motor actions. To accomplish such dynamic motion in a humanoid robot, the robot needs to simultaneously: 1) transfer high kinetic energy to the kicking leg, 2) maintain balance and stability of the entire body, and 3) manage the impact disturbance from the ball during the kicking moment. Prior studies on robotic soccer kicking often prioritized stability, leading to overly conservative quasi-static motions. In this work, we present a biomechanics-inspired control framework that leverages trajectory optimization and imitation learning to facilitate highly dynamic soccer kicks in humanoid robots. We conducted an in-depth analysis of human soccer kick biomechanics to identify key motion constraints. Based on this understanding, we designed kinodynamically feasible trajectories that are then used as a reference in imitation learning to develop a robust feedback control policy. We demonstrate the effectiveness of our approach through a simulation of an anthropomorphic 25 DoF bipedal humanoid robot, named PresToe, which is equipped with 7 DoF legs, including a unique actuated toe. Using our framework, PresToe can execute dynamic instep kicks, propelling the ball at speeds exceeding 11m/s in full dynamics simulation.

翻译：足球踢球是一种复杂的全身运动，需要多种动作的精细协调。为使仿人机器人完成此类动态运动，机器人需同时满足：1）将高动能传递至踢球腿，2）维持全身平衡与稳定性，3）处理踢球瞬间来自足球的冲击扰动。现有机器人足球踢球研究常优先考虑稳定性，导致产生过于保守的准静态动作。本研究提出一种仿生力学启发的控制框架，通过轨迹优化与模仿学习实现仿人机器人的高动态足球踢球。我们深入分析了人类足球踢球的生物力学特性以识别关键运动约束，在此基础上设计了运动学与动力学可行的轨迹，并将其作为模仿学习的参考来开发鲁棒的反馈控制策略。我们通过名为PresToe的25自由度拟人双足仿人机器人仿真验证了方法的有效性。该机器人配备7自由度腿部（包含独特的驱动式脚趾关节）。实验表明，PresToe能运用本框架执行动态脚背抽射，在全动力学仿真中将足球加速至超过11m/s的速度。