Tactile Velocity Estimation for Controlled In-Grasp Sliding

This letter studies the problem of controlling the sliding motion of an object held by a robot manipulator. We show how a parallel-jaw gripper can reliably control the motion of a rigid, prism-like object by 1) estimating the object's sliding velocity using measurements from tactile sensors at the gripper's fingertips and 2) controlling the grip strength to regulate the sliding velocity. We first train a neural network to estimate the sliding velocity from only tactile signals with data of tactile sensor measurements associated with various sliding velocities determined by an external motion capture system from repeated sliding trials of 28 different objects varying in size, shape, and surface texture. The velocity estimates from the neural network are then used as feedback for a closed-loop grip controller that maintains the desired sliding velocity. Experimental results show that our neural network estimates the object's sliding velocity with mean squared error under 0.5 $(\text{cm/s})^2$, generalizes well to objects of new shapes and surface textures, and enables our closed loop grip controller to reliably slide objects at different target velocities.