Robots usually establish contacts at rigid surfaces with near-zero relative velocities. Otherwise, impact-induced energy propagates in the robot's linkage and may cause irreversible damage to the hardware. Moreover, abrupt changes in task-space contact velocity and peak impact forces also result in abrupt changes in robot joint velocities and torques; which can compromise controllers' stability, especially for those based on smooth models. In reality, several tasks would require establishing contact with moderately high velocity. We propose to enhance task-space multi-objective controllers formulated as a quadratic program to be resilient to frictional impacts in three dimensions. We devise new constraints and reformulate the usual ones to be robust to the abrupt joint state changes mentioned earlier. The impact event becomes a controlled process once the optimal control search space is aware of: (1) the hardware-affordable impact bounds and (2) analytically-computed feasible set (polyhedra) that constrain post-impact critical states. Prior to and nearby the targeted contact spot, we assume, at each control cycle, that the impact will occur at the next iteration. This somewhat one-step preview makes our controller robust to impact time and location. To assess our approach, we experimented its resilience to moderate impacts with the Panda manipulator and achieved swift grabbing tasks with the HRP-4 humanoid robot.

翻译：机器人通常以接近零的相对速度在刚性表面建立接触。否则，冲击引起的能量会在机器人连杆中传播，并可能对硬件造成不可逆的损伤。此外，任务空间接触速度的突变和峰值冲击力也会导致机器人关节速度和力矩的突变，这可能破坏控制器的稳定性，尤其对于基于平滑模型的控制器而言。实际上，若干任务需要以中等较高的速度建立接触。我们提出增强以二次规划形式表述的任务空间多目标控制器，使其对三维摩擦冲击具有鲁棒性。我们设计了新的约束条件，并重新表述了常规约束条件，使其对上述关节状态的突变具有鲁棒性。一旦最优控制搜索空间能够感知以下两点，冲击事件便成为一个受控过程：(1) 硬件可承受的冲击界限，以及(2) 约束冲击后关键状态的解析计算可行集（多面体）。在目标接触点附近及之前，我们假设在每个控制周期中，冲击将在下一次迭代中发生。这种一步前馈机制使我们的控制器对冲击时间和位置具有鲁棒性。为评估我们的方法，我们使用Panda机械臂实验了其对中等冲击的鲁棒性，并借助HRP-4类人机器人实现了快速抓取任务。