We present a novel method for optimizing the posture of kinematically redundant torque-controlled robots to improve robustness during impacts. A rigid impact model is used as the basis for a configuration-dependent metric that quantifies the variation between pre- and post-impact velocities. By finding configurations (postures) that minimize the aforementioned metric, spikes in the robot's state and input commands can be significantly reduced during impacts, improving safety and robustness. The problem of identifying impact-robust postures is posed as a min-max optimization of the aforementioned metric. To overcome the real-time intractability of the problem, we reformulate it as a gradient-based motion task that iteratively guides the robot towards configurations that minimize the proposed metric. This task is embedded within a task-space inverse dynamics (TSID) whole-body controller, enabling seamless integration with other control objectives. The method is applied to a kinematically redundant aerial manipulator performing repeated point contact tasks. We test our method inside a realistic physics simulator and compare it with the nominal TSID. Our method leads to a reduction (up to 51% w.r.t. standard TSID) of post-impact spikes in the robot's configuration and successfully avoids actuator saturation. Moreover, we demonstrate the importance of kinematic redundancy for impact robustness using additional numerical simulations on a quadruped and a humanoid robot, resulting in up to 45% reduction of post-impact spikes in the robot's state w.r.t. nominal TSID.

翻译：本文提出了一种新颖的方法，用于优化运动学冗余的力矩控制机器人的姿态，以提升其在碰撞过程中的鲁棒性。我们采用刚性碰撞模型作为基础，构建了一种依赖于构型的度量指标，该指标量化了碰撞前后速度的变化。通过寻找使上述度量指标最小化的构型（姿态），可以显著降低碰撞期间机器人状态和输入指令的尖峰，从而提高安全性和鲁棒性。识别碰撞鲁棒姿态的问题被表述为上述度量指标的极小极大优化问题。为了解决该问题在实时计算上的困难，我们将其重新表述为一个基于梯度的运动任务，该任务迭代地引导机器人趋向于最小化所提度量指标的构型。此任务被嵌入到一个任务空间逆动力学（TSID）全身控制器中，从而能够与其他控制目标无缝集成。该方法被应用于执行重复点接触任务的运动学冗余空中机械臂。我们在一个逼真的物理模拟器中对本方法进行了测试，并与基准TSID方法进行了比较。我们的方法能够减少（相对于标准TSID最多减少51%）机器人构型在碰撞后的尖峰，并成功避免了执行器饱和。此外，我们通过对四足机器人和人形机器人进行额外的数值模拟，证明了运动学冗余对于碰撞鲁棒性的重要性，相对于基准TSID，机器人状态在碰撞后的尖峰最多可减少45%。