LeTac-MPC: Learning Model Predictive Control for Tactile-reactive Grasping

Understand

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 to 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 information on the physical properties and states of the grasped object.