Disambiguate Gripper State in Grasp-Based Tasks: Pseudo-Tactile as Feedback Enables Pure Simulation Learning
The research tackles ambiguity in robotic grasping where a gripper's binary state cannot distinguish between successful and empty grasps. By using pseudo-tactile feedback from a force-controlled gripper, the system can reliably determine if an object is held. This disambiguation allows policies to use noise-free binary observations, enabling high performance through pure simulation learning without needing real-world data collection.