Learning Force-Regulated Robotic Manipulation with a Low-Cost Tactile-Force-Controlled Gripper
The research developed a low-cost tactile-force gripper and a policy framework called RETAF to enable robots to learn precise, high-frequency force regulation during manipulation. RETAF decouples predicting arm position from predicting grasping force, allowing the system to react quickly to tactile feedback for stable object handling.