Unknown payloads can strongly affect compliant robotic manipulation, especially when the payload center of mass is not aligned with the tool center point. In this case, the payload generates an offset wrench at the robot wrist. During motion, this wrench is not only related to payload weight, but also to payload inertia. If it is not modeled, the compliant controller can interpret it as an external interaction wrench, which causes unintended compliant motion, larger tracking error, and reduced transport accuracy. This paper presents a wrench-aware admittance control framework for unknown-payload pick-and-place using a UR5e robot. The method uses force-torque measurements in two different roles. First, a three-axis translational excitation term is used to reduce payload-induced force effects during transport without making the robot excessively stiff. Second, after grasping, the controller first estimates payload mass for transport compensation and then estimates the payload CoM offset relative to the TCP using wrist force-torque measurements collected during the subsequent translational motion. This helps improve object placement and stacking behavior. Experimental results show improved transport and placement performance compared with uncorrected placement while preserving compliant motion.

翻译：未知负载会严重影响柔顺机器人操作，尤其当负载质心与工具中心点不重合时。此时，负载会在机器人手腕处产生偏置力矩。运动过程中，该力矩不仅与负载重量相关，还与负载惯性相关。若未对其建模，柔顺控制器会将其误判为外部交互力矩，导致非预期的柔顺运动、跟踪误差增大及搬运精度下降。本文提出一种面向未知负载抓取放置操作的手腕力矩感知导纳控制框架，并采用UR5e机器人实现。该方法利用力/力矩测量实现两种功能：首先，通过三轴平移激励项降低搬运过程中负载引起的力效应，避免机器人过度刚性；其次，控制器在抓取后先估算负载质量以进行搬运补偿，再根据后续平移运动中采集的手腕力/力矩数据估算负载质心相对于TCP的偏移量，从而改善物体放置与堆叠行为。实验结果表明，与未校正的放置操作相比，本方法在保持柔顺运动的同时提升了搬运与放置性能。