MiniBEE: A New Form Factor for Compact Bimanual Dexterity

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. This traditional framework increases system complexity and footprint while only exploiting a fraction of the overall workspace for dexterous interaction. We introduce the OURS (OURSLONG), 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 and enables the entirety of systems workspace to be used for dexterity. To 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.