MiniBEE: A New Form Factor for Compact Bimanual Dexterity

2025-10-01

AI Summary

Conventional dual-arm robotic systems rely on two high-degree-of-freedom (DoF) arms for dexterous cooperation, resulting in structural complexity, poor portability, and low workspace utilization. Method: We propose MiniBEE—a compact, wearable dual-phone robotic system—where two low-DoF arms are kinematically coupled into a closed-chain mechanism enabling full relative pose control between grippers. We design lightweight end-effectors and a novel kinematic dexterity metric, jointly optimizing workspace coverage and manipulation performance. MiniBEE supports two complementary operation modes: wearable teleoperation and master–slave coordination. Leveraging kinematic modeling, self-tracking pose estimation, and imitation learning, we establish an end-to-end bimanual manipulation pipeline. Results: Experimental evaluation in real-world scenarios demonstrates robust performance, achieving significant improvements in portability, dexterity, and teaching efficiency compared to conventional approaches.

Technology Category

• Intelligent Robots: Manipulation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Teamwork

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

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 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.

Problem

• Developing compact bimanual robot system with reduced mobility arms
• Formulating kinematic metric to maximize dexterous workspace while lightweight
• Enabling wearable data collection and imitation learning for manipulation

Innovation

• Compact coupled kinematic chain with reduced-mobility arms
• Novel dexterity metric enabling lightweight wearable design
• Dual-mode system supporting wearable demonstration and robot deployment