Abstract-- Bimanual robot manipulators can achieve impressive dexterity, but typically rely on two full six-or seven-degree-of-freedom arms so that paired grippers can coordinate effectively. We introduce the MiniBEE (Miniature Bimanual End-effector), a compact system in which two reduced-mobility arms (3+ DOF each) are coupled into a kinematic chain that preserves full relative positioning between grippers. T o guide our design, we formulate a kinematic dexterity metric that enlarges the dexterous workspace while keeping the mechanism lightweight and wearable. The resulting system supports two complementary modes: (i) wearable kinesthetic data collection with self-tracked gripper poses, and (ii) deployment on a standard robot arm, extending dexterity across its entire workspace. We present kinematic analysis and design optimization methods for maximizing dexterous range, and demonstrate an end-to-end pipeline in which wearable demonstrations train imitation learning policies that perform robust, real-world bimanual manipulation. In recent years, bimanual robotic manipulators have shown remarkable dexterity. The combination of imitation learning from human demonstrations and two well-articulated kinematic chains has enabled such systems to use simple parallel grippers to autonomously perform highly dexterous tasks [1]-[7], with robustness to initial conditions or perturbations encountered during execution [8]-[10]. To achieve these results, current systems typically rely on the combination of two 6-or 7-degree-of-freedom (DOF) robotic arms.