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，以提升其在冲击过程中的鲁棒性。我们采用刚性冲击模型作为基础，构建了一个依赖于构型的度量指标，该指标量化了冲击前后速度的变化。通过寻找使上述度量指标最小化的构型（姿态），可以显著降低冲击过程中机器人状态及输入指令的尖峰，从而提高安全性与鲁棒性。识别冲击鲁棒姿态的问题被表述为上述度量指标的最小-最大优化。为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朝向最小化所提度量指标的构型。此任务被嵌入到任务空间逆动力学（TSID）全身控制器中，从而能够与其他控制目标无缝集成。该方法被应用于一个执行重复点接触任务的运动学冗余空中机械臂。我们在一个高保真物理仿真器中测试了该方法，并与基准TSID控制器进行了对比。我们的方法使得机器人构型在冲击后的尖峰显著降低（相对于标准TSID最高降低51%），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仿真，证明了运动学冗余对于冲击鲁棒性的重要性，结果显示机器人状态在冲击后的尖峰相对于基准TSID最高可减少45%。