This paper presents a novel control approach to dealing with object slip during robotic manipulative movements. Slip is a major cause of failure in many robotic grasping and manipulation tasks. Existing works increase grip force to avoid/control slip. However, this may not be feasible when (i) the robot cannot increase the gripping force -- the max gripping force is already applied or (ii) increased force damages the grasped object, such as soft fruit. Moreover, the robot fixes the gripping force when it forms a stable grasp on the surface of an object, and changing the gripping force during real-time manipulation may not be an effective control policy. We propose a novel control approach to slip avoidance including a learned action-conditioned slip predictor and a constrained optimiser avoiding a predicted slip given a desired robot action. We show the effectiveness of the proposed trajectory adaptation method with receding horizon controller with a series of real-robot test cases. Our experimental results show our proposed data-driven predictive controller can control slip for objects unseen in training.

翻译：本文为处理机器人操控运动过程中的天体滑落提供了一种新的控制方法。 滑动是许多机器人掌握和操纵任务失败的主要原因。 现有的工程增加了控制力以避免/ 控制滑落。 但是, 当 (一) 机器人无法增加控制力, 最大控制力已经应用了, 或者 (二) 增加了对所捕捉的物体( 如软果) 的伤害。 此外, 机器人在物体表面形成稳定捕捉力时修补控制力, 实时操作时改变控制力可能不是一个有效的控制政策。 我们提议了一种新的控制方法, 以滑落, 包括一个学习的、 以行动为条件的滑动预测器和 受限的美化器, 避免一个预言的滑落, 以想要的机器人动作。 我们用一系列真实的机器人测试案例展示了拟议轨迹控制器的效用。 我们的实验结果表明, 我们提议的数据驱动预测控制器可以在训练中控制看不见的物体。