We propose a robotic manipulation system that can pivot objects on a surface using vision, wrist force and tactile sensing. We aim to control the rotation of an object around the grip point of a parallel gripper by allowing rotational slip, while maintaining a desired wrist force profile. Our approach runs an end-effector position controller and a gripper width controller concurrently in a closed loop. The position controller maintains a desired force using vision and wrist force. The gripper controller uses tactile sensing to keep the grip firm enough to prevent translational slip, but loose enough to induce rotational slip. Our sensor-based control approach relies on matching a desired force profile derived from object dimensions and weight and vision-based monitoring of the object pose. The gripper controller uses tactile sensors to detect and prevent translational slip by tightening the grip when needed. Experimental results where the robot was tasked with rotating cuboid objects 90 degrees show that the multi-modal pivoting approach was able to rotate the objects without causing lift or slip, and was more energy-efficient compared to using a single sensor modality and to pick-and-place. While our work demonstrated the benefit of multi-modal sensing for the pivoting task, further work is needed to generalize our approach to any given object.

翻译：我们提出了一种结合视觉、腕部力觉与触觉感知的机器人操控系统，能够实现物体在表面上的枢轴旋转。该方法通过在维持期望腕部力分布的同时允许旋转滑动，实现对平行夹爪抓取点周围物体旋转的精准控制。该系统中，末端执行器位置控制器与夹爪宽度控制器并行构成闭环控制：位置控制器利用视觉与腕部力觉维持期望力作用；夹爪控制器则通过触觉传感确保夹持力足以防止平移滑动，但松弛到足以诱发旋转滑动。本传感器融合控制方法的核心在于：基于物体尺寸与重量推导期望力分布，并配合视觉监测的物体姿态进行匹配。夹爪控制器利用触觉传感器检测并防止平移滑动，必要时收紧夹持力。实验将机器人设定为旋转长方体物体90度，结果表明，多模态枢轴方法能够在不引发抬升或滑动的情况下完成旋转，且相比单一传感器模态及拾放操作具有更优能效。虽然本研究验证了多模态传感在枢轴任务中的优势，但未来仍需进一步研究以将该方法推广至任意物体。