  Grasping is a crucial task in robotics, necessitating tactile feedback and
reactive grasping adjustments for robust grasping of objects under various
conditions and with differing physical properties. In this paper, we introduce
LeTac-MPC, a learning-based model predictive control (MPC) for tactile-reactive
grasping. Our approach enables the gripper grasp objects with different
physical properties on dynamic and force-interactive tasks. We utilize a
vision-based tactile sensor, GelSight, which is capable of perceiving
high-resolution tactile feedback that contains the information of physical
properties and states of the grasped object. LeTac-MPC incorporates a
differentiable MPC layer designed to model the embeddings extracted by a neural
network (NN) from tactile feedback. This design facilitates convergent and
robust grasping control at a frequency of 25 Hz. We propose a fully automated
data collection pipeline and collect a dataset only using standardized blocks
with different physical properties. However, our trained controller can
generalize to daily objects with different sizes, shapes, materials, and
textures. Experimental results demonstrate the effectiveness and robustness of
the proposed approach. We compare LeTac-MPC with two purely model-based
tactile-reactive controllers (MPC and PD) and open-loop grasping. Our results
show that LeTac-MPC has the best performance on dynamic and force-interactive
tasks and the best generalization ability. We release our code and dataset at
https://github.com/ZhengtongXu/LeTac-MPC.
