Xueyun Tang and Xiaomeng TangによるVega Connect ロボット制御インターフェース

Xueyun Tang and Xiaomeng TangによるVega Connect ロボット制御インターフェースは、スマートリビング・ホームオートメーションデザインコンペティションカテゴリーにおける非常に権威ある2025 A' Design Award 銀賞の優れた受賞作品です。

銀スマートリビング· 2025

Vega Connect ロボット制御インターフェース

デザイナー： Xueyun Tang and Xiaomeng Tang

クライアント: Dexmate Inc.

スマートリビングカテゴリーのA' Design Award 銀賞受賞者

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デザイン

Vega Connect ロボット制御インターフェースについて

Vega Connectは、家庭ユーザー向けに設計されたロボット制御インターフェースです。本システムは、モバイルアプリケーションとウェブポータルを組み合わせて構成されています。モバイルアプリケーションでは、日常的な言葉で家事を依頼でき、その要求はロボットの精密な動作へと変換され、進捗状況がリアルタイムで表示されます。三次元のデジタルツインがロボットとその周囲の環境を映し出し、遠隔からの監視を可能にします。ウェブポータルでは、各家庭に合わせたカスタムルーティンを作成できます。専門的な訓練がなくても高度なロボットを使いこなせるようにすることを目指しています。

受賞

A' Design Award 銀賞について

Vega Connect ロボット制御インターフェースは、見事で印象的なハイエンドのデザインに授与される、非常に権威ある評価である A' Design Award 銀賞の受賞作品です。銀賞の受賞作品は、きわめて輝かしく創造性に富んだ作品であり、デザイナーの傑出した専門知識、才能、洞察力を示しています。Vega Connect ロボット制御インターフェースは、素晴らしい代表作であり、驚くほどプロフェッショナルで、深く革新的であることが期待されます。この A' Design Award 銀賞を受賞したデザインは、非常に注目すべき称賛される発明であり、芸術・科学・デザイン・技術の境界に寄与し、それを前進させます。Vega Connect ロボット制御インターフェースは、多くの人に羨まれる創造物であり、欲求を呼び覚まし、前向きな感情をもたらし、驚きと感嘆を灯します。

デザインの裏側

プレスキット

デザイナーが提供するクレジット、チームメンバー、技術的な詳細。

独自の特性: 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.
寸法とパッケージ: 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.
タグ: 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
製造技術: 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.
デザインの課題: 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.
リサーチ: 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.
インスピレーション: 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.
プロジェクト期間: 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.
オペレーションフロー: 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.
特許: Copyrights belong to Dexmate Inc., 2025
チームメンバー: Xueyun Tang
Xiaomeng Tang
Xi Pang
Yuzhe Qin
Tao Chen
画像クレジット: Dexmate Inc.