  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.
