This paper proposes a combined optimization and learning method for impact-friendly, non-prehensile catching of objects at non-zero velocity. Through a constrained Quadratic Programming problem, the method generates optimal trajectories up to the contact point between the robot and the object to minimize their relative velocity and reduce the impact forces. Next, the generated trajectories are updated by Kernelized Movement Primitives, which are based on human catching demonstrations to ensure a smooth transition around the catching point. In addition, the learned human variable stiffness (HVS) is sent to the robot's Cartesian impedance controller to absorb the post-impact forces and stabilize the catching position. Three experiments are conducted to compare our method with and without HVS against a fixed-position impedance controller (FP-IC). The results showed that the proposed methods outperform the FP-IC while adding HVS yields better results for absorbing the post-impact forces.

翻译：本文提出了一种结合优化与学习的非零速度抗冲击非抓取式物体抓取方法。通过求解带约束的二次规划问题，该方法生成机器人到达物体接触点前的最优轨迹，以最小化二者相对速度并降低冲击力。随后，基于人类抓取示教构建的核化运动基元对生成的轨迹进行更新，确保抓取点附近的平滑过渡。此外，将学习得到的人体变刚度参数输入至机器人笛卡尔阻抗控制器，以吸收抓取后冲击力并稳定抓取位置。通过三组实验，将本文方法（含/不含人体变刚度）与固定位置阻抗控制器进行对比。结果表明，所提方法优于固定位置阻抗控制器，且引入人体变刚度后吸收冲击力的效果更优。