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：我们提出了一种利用视觉、腕部力觉与触觉传感实现物体在支撑面旋转的机器人操控系统。该系统旨在通过允许转动滑移，在维持期望腕部力分布的同时，控制物体围绕平行夹持器夹持点进行旋转。我们采用末端执行器位置控制器与夹持器宽度控制器协同工作的闭环架构，其中位置控制器基于视觉与腕部力信息维持期望力，夹持器控制器利用触觉传感确保夹持力足以防止平移滑移，但松弛至可诱发转动滑移的程度。本传感控制方法通过匹配基于物体尺寸与重量推导的期望力分布，并结合视觉监测物体位姿实现调控。夹持器控制器通过触觉传感器实时检测并阻止平移滑移，必要时自动收紧夹持力。实验设置机器人执行立方体物体90度旋转任务，结果表明：多模态旋转方法可在避免物体抬升或滑移的前提下完成旋转，且相比单一传感模态与抓取-放置策略具有更高能效。本研究虽证实了多模态传感在旋转任务中的优势，但需进一步研究以将该方法推广至任意物体。