论文标题
基于联合优化和学习的非零速度捕获对撞击友好的对象
Impact-Friendly Object Catching at Non-Zero Velocity Based on Combined Optimization and Learning
论文作者
论文摘要
本文提出了一种合并的优化和学习方法，用于以非零速度对物体进行效果友好，非划出的捕获。通过受约束的二次编程问题，该方法生成最佳轨迹，直到机器人和对象之间的接触点，以最大程度地减少其相对速度并降低影响力。接下来，生成的轨迹是由内核运动原始素更新的，该动作原始基于人类的捕捉演示，以确保围绕捕获点的平稳过渡。此外，学习的人类可变刚度（HVS）被发送到机器人的笛卡尔阻抗控制器，以吸收后影响力并稳定捕获位置。进行了三个实验，以将我们的方法与固定位置阻抗控制器（FP-IC）进行比较。结果表明，所提出的方法在添加HVS的同时优于FP-IC，可以更好地吸收影响后力。
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.
