Enterprise AI Analysis
See-Control: A Multimodal Agent Framework for Smartphone Interaction with a Robotic Arm
This analysis focuses on 'See-Control,' a novel framework enabling MLLM-based embodied agents to operate smartphones via a low-DoF robotic arm. It offers a platform-agnostic, privacy-preserving solution by relying on physical interaction and screen imagery, moving beyond ADB-dependent methods. The framework includes an ESO benchmark, an MLLM-based agent generating robotic controls, and a richly annotated dataset.
Executive Impact
Key performance indicators demonstrating the potential of this technology in real-world applications.
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Platform Agnostic
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Enterprise Process Flow
User Instruction
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MLLM Reasoning & GUI Navigation
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Visual Perception Tools (Text/Icon Detection)
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Robot Arm Action Generation
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Physical Interaction
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Overall Success Rate (SR) on ESO Benchmark
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ESO Task: Setting a Calendar Reminder
An example task demonstrating See-Control's capability for a real-world scenario.
Challenge: Searching for a specific date (Winter Olympics opening ceremony) in Chrome and then creating a calendar event with that date, all via physical robotic arm interactions.
Solution: The agent uses text recognition for search input, icon detection for app navigation (Chrome, Calendar), and precise tap/type actions to input dates and confirm events, navigating various UI elements without ADB.
Outcome: Successfully identified the date (Feb 6, 2026) and created the calendar reminder, showcasing robust visual perception and action execution in a multi-step, multi-app scenario.
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