Vega Connect 機械人控制介面 by Xueyun Tang and Xiaomeng Tang

Vega Connect 機械人控制介面 by Xueyun Tang and Xiaomeng Tang is a highly esteemed winner of the very prestigious 2025 A' Design Award 銀獎 in 智能生活設計 Competition Category.

銀智能生活· 2025

Vega Connect 機械人控制介面

by Xueyun Tang and Xiaomeng Tang

Client: Dexmate Inc.

A' Design Award 銀獎 Winner in 智能生活 Category

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The Design

About Vega Connect 機械人控制介面

Vega Connect 係一個專為家庭用戶而設嘅機械人控制介面。系統結合咗流動應用程式同網上平台。流動應用程式令用戶可以用日常語言提出家務要求，每個要求都會轉化為精確嘅機械人動作，並實時顯示進度。三維數碼分身可以展示機械人同佢周圍嘅環境，方便遙距監控。網上平台可以為每個家庭建立自訂嘅日常程序。設計目標係令用戶毋須接受技術培訓都可以使用先進嘅機械人。

The Recognition

About the A' Design Award 銀獎

Vega Connect 機械人控制介面是 A' Design Award 銀獎得主，這是授予頂級、華美而令人印象深刻的設計的極為崇高的認可。銀獎得主是極其卓越、極具創造力的作品，展現出其設計師傑出的專業技能、才華與敏銳。預計Vega Connect 機械人控制介面將成為一件出色的典範之作，兼具非凡的專業性與深刻的創新性。這件贏得 A' Design Award 銀獎的設計是一項備受矚目、令人欽佩的發明，造福並拓展藝術、科學、設計和技術的界限。Vega Connect 機械人控制介面是一件令人羨慕的創作，它喚起渴望，帶來積極的情感，並激發驚嘆與讚嘆。

Behind the Design

Press Kit

Credits, team members and technical details provided by the designer.

Unique Properties: Vega Connect redefines human-robot interaction through its pioneering dual-interface system that bridges advanced robotics with an intuitive user experience. Unlike conventional systems requiring technical expertise, our solution transforms complex robotic programming into natural interactions accessible to all users regardless of technical background. The mobile interface enables effortless task delegation through conversational commands, while the complementary web portal offers sophisticated customization capabilities. This architecture represents a paradigm shift in robotics—creating technology that adapts to human communication patterns rather than forcing users to adapt to machines, thereby democratizing access to revolutionary capabilities previously reserved for specialists.
Dimensions & Packaging: The Vega Connect system integrates multimodal interaction capabilities enabling humans to control robots via natural language, hand gestures, and facial expressions through advanced LLM/VLM models. The 3D digital twin technology provides immersive visualization of robot operations and surroundings, facilitating seamless remote monitoring and control across unlimited distances. The middleware abstracts complex robotics commands into intuitive interactions, allowing users to automate routine tasks through conversational interfaces and scheduling functionality.
Tags: Human-robot interaction, robot interaction system, home robot interface, AI-driven automation, personal robotics, dual-interface system, intuitive robot control, natural language interaction, general-purpose robot
Production Technology: Vega Connect bridges the gap in robotics: industrial robots need expertise, while consumer robots are single-purpose. As AI general-purpose robots tackle diverse tasks, seamless human-robot interaction is essential. Dexmate team has developed a multimodal interaction framework utilizing natural language processing, gesture recognition, and intuitive web interfaces. The system employs real-time 3D visualization with digital twin technology, implementing semantic mapping between human commands and robotic execution paths.
Design Challenge: The fundamental challenge was reconceptualizing robot interaction for non-technical users while maintaining advanced functionality. Historically, robotics interfaces prioritized engineering precision over usability, creating significant adoption barriers. Our team struggled to develop a system that satisfied both the accessibility needs of everyday users and the precision requirements of complex robotic operations. The breakthrough came through extensive user testing revealing distinct interaction patterns across demographics, leading to our dual-interface architecture. Technical obstacles included developing reliable natural language processing for household contexts and creating abstraction layers that translated simple commands into complex motion sequences while maintaining safety parameters.
Research: Our user-centered research combined ethnographic observation and usability testing with 35 households across three generations. We identified critical barriers in robotics adoption through 250+ hours of in-home studies using eye-tracking and think-aloud protocols. Testing revealed traditional interfaces achieved only 27% task completion rates versus 89% with natural language commands. This directly shaped Vega Connect's dual-interface approach, adapting to human behavior rather than forcing humans to adapt to technology.
Inspiration: The Vega Connect project addresses a fundamental disparity in robotics: while AI-powered robots grow increasingly capable, their interfaces remain engineered for specialists rather than everyday users. Recognizing that these advanced robots will soon enter domestic settings, Dexmate developed an intuitive interaction system that bridges this accessibility gap. Our solution enables non-technical users to communicate naturally with sophisticated robotic systems, democratizing access to this transformative technology.
Project Duration: The Vega Connect robot-control interactive system design and its user testing began in November 2024 in California, USA, and concluded in March 2025. Technical implementation for both web and mobile platforms commenced in January 2025 and is currently ongoing in preparation for a wide market release.
Operation Flow: Vega Connect transforms robot control from complex programming to natural conversation. Users simply open the mobile app and request tasks using everyday language or intuitive taps—"clean the kitchen" or "fold laundry"—eliminating technical barriers. The system translates these simple requests into precise robotic actions while providing real-time progress updates. For personalization, the complementary web portal allows users to easily create custom routines specific to their home environment. This dual approach delivers superior results by adapting to human communication patterns rather than forcing users to learn technical commands, making advanced robotics accessible to everyone regardless of technical expertise.
Patents: Copyrights belong to Dexmate Inc., 2025
Team Members: Xueyun Tang
Xiaomeng Tang
Xi Pang
Yuzhe Qin
Tao Chen
Image Credits: Dexmate Inc.