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 while only exploiting a fraction of the overall workspace for dexterous interaction. 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. 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.

翻译：双臂机器人操作器可实现令人瞩目的灵巧性，但通常依赖两个完整的六或七自由度手臂，以使配对夹爪能够有效协调。这种传统框架增加了系统复杂度，同时仅利用了整体工作空间中的一小部分进行灵巧交互。我们提出了MiniBEE（微型双手末端执行器），这是一种紧凑型系统，其中两个低移动性手臂（各3+自由度）耦合成一个运动学链，从而保持夹爪之间的完整相对定位。为指导设计，我们制定了一个运动学灵巧度指标，在扩大灵巧工作空间的同时保持机构轻量化和可穿戴性。最终系统支持两种互补模式：（i）利用自追踪夹爪位姿进行可穿戴运动学数据采集，以及（ii）部署于标准机器人手臂上，将灵巧性扩展至其整个工作空间。我们提出了用于最大化灵巧范围的运动学分析与设计优化方法，并展示了端到端流程：通过可穿戴演示训练模仿学习策略，从而执行鲁棒的、真实世界的双手操作任务。