Robots Get a Grip: Teaching Arms to Use Smartphones

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Author: Denis Avetisyan

Researchers have developed a new framework allowing robotic arms to physically interact with smartphones, bypassing traditional software-based control methods.

See-Control reimagines mobile human-computer interaction automation not as a software problem, but as an embodied challenge of perception and action-leveraging a richly labeled dataset and an MLLM-based agent to circumvent platform limitations and enhance security, mirroring the nuanced ways humans naturally engage with smartphones.
See-Control reimagines mobile human-computer interaction automation not as a software problem, but as an embodied challenge of perception and action-leveraging a richly labeled dataset and an MLLM-based agent to circumvent platform limitations and enhance security, mirroring the nuanced ways humans naturally engage with smartphones.

See-Control enables privacy-preserving, cross-platform smartphone operation via a low-degree-of-freedom robotic arm, utilizing multimodal vision-language-action models.

While recent advances leverage multimodal large language models for smartphone automation, existing methods remain tethered to platform-specific software like the Android Debug Bridge. This limitation motivates the development of See-Control: A Multimodal Agent Framework for Smartphone Interaction with a Robotic Arm, a novel framework enabling robotic operation of smartphones via direct physical interaction with a low-DoF arm-circumventing the need for privileged system access. By introducing a new benchmark, richly annotated dataset, and an MLLM-based embodied agent, See-Control offers a cross-platform and privacy-preserving alternative for robotic smartphone control. Could this approach pave the way for more versatile and accessible home robots capable of seamlessly integrating with our digital lives?

The Inevitable Friction of Control

Smartphone automation frequently depends on the Android Debug Bridge (ADB), a tool initially designed for developers. While powerful, this reliance creates notable obstacles; ADB is primarily geared towards Android, limiting the potential for seamless control across different mobile operating systems like iOS. More critically, utilizing ADB often necessitates ‘adb access’, which effectively bypasses standard security protocols and grants extensive permissions to the controlling computer. This privileged access raises substantial privacy concerns, as it potentially allows unauthorized access to sensitive user data and control over device functions. The inherent vulnerabilities associated with ADB highlight the need for automation frameworks that prioritize user privacy and offer broader platform compatibility, moving beyond the limitations of developer-focused tools.

Contemporary smartphone automation techniques frequently demand elevated device privileges – often rooted or jailbroken access – to effectively manipulate system functions. This reliance introduces substantial hurdles to widespread adoption, as most users are understandably hesitant to compromise their device’s security posture. Granting such extensive permissions not only voids warranties but also opens avenues for malicious actors to exploit vulnerabilities and gain unauthorized control. The inherent risk associated with privileged access creates a significant barrier, limiting automation’s reach and fostering legitimate concerns about data privacy and device integrity; consequently, a shift towards methods requiring minimal or no such access is crucial for realizing the full potential of smartphone automation while maintaining user trust and security.

The limitations of current smartphone automation techniques underscore a critical need for innovation in how devices are controlled. Existing systems, often reliant on tools like the Android Debug Bridge, present obstacles to widespread usability due to compatibility issues and inherent security risks associated with requiring privileged access. A truly versatile approach would transcend platform boundaries, allowing seamless control across diverse devices and operating systems. Simultaneously, prioritizing user privacy is essential; future automation frameworks must minimize data exposure and operate without necessitating deep-level device permissions. Ultimately, a more accessible paradigm – one that empowers users with simple, secure, and cross-platform control – is paramount to unlocking the full potential of smartphone automation and integrating these devices more effectively into daily life.

A System Grown, Not Built: Introducing See-Control

See-Control utilizes a low-degree-of-freedom robotic arm to enable physical interaction with smartphones, governed by a Multimodal Large Language Model (MLLM). This approach distinguishes itself by entirely removing the dependency on the Android Debug Bridge (ADB). Traditionally, ADB is required for external software to control smartphone functions; See-Control circumvents this requirement by directly manipulating the touchscreen via the robotic arm, translating MLLM-interpreted user instructions into physical actions. This direct manipulation enables operation independent of operating system-level debugging protocols, providing a novel method for automation and control.

See-Control utilizes Multimodal Large Language Models (MLLMs) to bridge the gap between natural language instructions and physical smartphone interactions. The framework processes user input, employing the MLLM’s reasoning abilities to determine the intended action – such as tapping a specific button or scrolling to a particular item. This interpreted intent is then translated into a sequence of low-level motor commands for a robotic arm, enabling it to accurately perform the desired action on the smartphone touchscreen. The MLLM effectively functions as a task planner, decomposing complex user requests into executable physical steps without requiring pre-programmed actions or reliance on application-specific APIs.

See-Control improves user privacy and expands operating system compatibility by removing the requirement for the Android Debug Bridge (ADB). Traditional automation frameworks relying on ADB necessitate granting significant permissions and access to the device, creating potential security vulnerabilities. See-Control operates visually, emulating user interactions directly on the screen, thus bypassing these permissions and enabling operation on platforms where ADB is unavailable, such as iOS. The framework’s functionality and robustness are validated through a dedicated dataset of 155 annotated tasks, utilized for both training the Multimodal Large Language Model and evaluating its performance across diverse smartphone automation scenarios.

The See-Control Agent processes user instructions and screen images via a visual perception module to generate actions, as demonstrated by its ability to accurately locate and interact with UI elements through specialized visual grounding and prompting.
The See-Control Agent processes user instructions and screen images via a visual perception module to generate actions, as demonstrated by its ability to accurately locate and interact with UI elements through specialized visual grounding and prompting.

The Ghosts in the Machine: Decoding the Visual World

The See-Control system’s Visual Perception Module employs advanced object detection models, prominently featuring Grounding DINO, to achieve precise identification and localization of Graphical User Interface (GUI) elements on the screen. This model facilitates the recognition of visual components such as buttons, sliders, and input fields by analyzing pixel data and generating bounding boxes around detected objects. The bounding boxes provide coordinate data representing the location and dimensions of each GUI element, enabling subsequent interaction and control by the system. Grounding DINO’s architecture allows for robust performance in identifying objects even under varying conditions such as differing lighting, occlusion, and scale.

Initial investigations into image recognition capabilities for the See-Control system considered the CLIP model; however, testing revealed Grounding DINO to be significantly more effective within the constraints of a smartphone user interface. Specifically, Grounding DINO demonstrated superior performance in accurately identifying and classifying icons, and in comprehending the overall visual scene presented on the screen. This improved robustness is attributed to Grounding DINO’s architecture, which provides more precise object localization and a greater capacity for handling the complex and often cluttered visual information typical of mobile devices.

The See-Control system incorporates an Optical Character Recognition (OCR) model to facilitate the detection and interpretation of textual elements present on the device screen. This OCR component processes visual data to identify characters and convert them into machine-readable text, enabling the system to understand labels, button text, and other interface content. The resulting text data is then utilized to augment the system’s overall understanding of the GUI, supporting more accurate element identification and interaction capabilities beyond purely visual analysis.

This execution pathway demonstrates the See-Control agent's ability to integrate task planning, reasoning, and visual perception to successfully complete a task.
This execution pathway demonstrates the See-Control agent’s ability to integrate task planning, reasoning, and visual perception to successfully complete a task.

The Inevitable Limits of Control, and the Seeds of Future Systems

The current iteration of the See-Control system utilizes a robotic arm constrained to single-touch interactions, which fundamentally limits the complexity of smartphone operations it can effectively perform. This restriction means actions requiring gestures – such as pinching to zoom, swiping, or rotating – are beyond its current capabilities, necessitating a simplification of tasks for successful execution. While the system adeptly handles single-point interactions like tapping buttons or selecting list items, the inability to perform multi-touch actions represents a significant hurdle in replicating the full spectrum of human-smartphone interaction. Future advancements hinge on overcoming this limitation to allow for more nuanced and comprehensive control, ultimately bridging the gap between assistive technology and natural smartphone usage.

See-Control establishes the potential for operating a smartphone remotely through robotic manipulation, achieving what is termed embodied smartphone operation (ESO) without relying on the Android Debug Bridge (ADB). While current performance metrics – Success Rate (SR), Completion Rate (CR), and Step Efficiency (SE) – are demonstrably influenced by task complexity, the system successfully executes smartphone actions despite these challenges. Specifically, as tasks demand more intricate sequences or precise interactions, both the likelihood of successful completion (SR) and the rate at which tasks are fully finished (CR) decrease. Furthermore, Step Efficiency, a measure of how directly the robotic arm completes a task, is lower for complex actions, suggesting a need for optimized movement planning and control as task demands increase. These findings, even with present limitations, highlight a significant step towards truly remote and embodied smartphone interaction.

The evolution of See-Control hinges on advancements in robotic dexterity and interaction methods; future iterations will prioritize the integration of more sophisticated robotic manipulators capable of nuanced movements beyond single-touch operations. This expansion will be coupled with the exploration of multi-touch capabilities, allowing the system to mimic the full range of human smartphone interactions – from complex gestures and pinch-to-zoom actions to simultaneous inputs. By overcoming the limitations of current single-touch functionality, researchers aim to unlock the potential for truly embodied smartphone operation, enabling users to perform a wider variety of tasks with increased efficiency and naturalness. This development promises to extend the applicability of ADB-free control to more complex mobile applications and user interfaces, paving the way for assistive technologies and remote control solutions.

The See-Control agent iteratively observes its environment, reasons about the current state, and executes precise robotic arm actions, as demonstrated in the first four steps of a task.
The See-Control agent iteratively observes its environment, reasons about the current state, and executes precise robotic arm actions, as demonstrated in the first four steps of a task.

The pursuit of seamless human-robot interaction, as demonstrated by See-Control, echoes a timeless challenge in system design. The framework’s emphasis on operating smartphones without privileged access-a deliberate sidestepping of traditional ADB methods-reveals a deeper truth about control. As David Hilbert observed, “We must be able to answer the question: what are the ultimate foundations of mathematics?” This applies equally well to robotics; what are the foundational assumptions about access and control upon which we build these systems? See-Control’s approach-a low-DoF arm and vision-language-action models-doesn’t solve the problem of complexity, it navigates within it, accepting the inherent limitations and crafting a solution that respects the boundaries of the existing ecosystem. Every architectural choice, even one that appears restrictive, is a prophecy of future resilience.

What Lies Ahead?

See-Control represents not a solution, but a carefully constructed narrowing of the problem. The framework sidesteps the inevitability of software dependence-the endless patching and privilege escalation-by embracing physical interaction. Yet, this very act merely shifts the locus of fragility. The low-DoF arm, a compromise born of practicality, will inevitably encounter states the system was not designed to handle. These won’t be failures, of course; they will be novel configurations, emergent behaviors born of imperfect execution in a messy world. Long stability is the sign of a hidden disaster; the system will evolve into unexpected shapes.

The emphasis on privacy is commendable, a recognition that every connected device is a potential vector for surveillance. However, the information gleaned from observing physical interaction-the subtle pressures, the hesitations, the corrections-may prove just as revealing. The illusion of control is a powerful one. The true challenge isn’t building a system that can operate a smartphone, but understanding the implications of a system that observes how a human does.

Future work will undoubtedly focus on expanding the arm’s dexterity and the range of supported devices. But a more fruitful path lies in accepting the inherent limitations of such a system. Instead of striving for perfect emulation of human behavior, the focus should be on augmenting it-on creating a symbiosis where the robot handles the tedious, the repetitive, and the physically demanding, freeing the human to focus on the unpredictable and the creative. Systems don’t fail-they evolve.

Original article: https://arxiv.org/pdf/2512.08629.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-12-11 00:59