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This project is a computer control framework that uses multimodal vision models to simulate mouse and keyboard inputs for automating desktop tasks. It functions as an autonomous agent and vision-based orchestrator that interprets screen visuals to interact with user interfaces. The system employs vision language models and object detection to locate and click interface elements. It utilizes visual grounding to overlay numerical markers on UI components and uses optical character recognition to map on-screen text to precise pixel coordinates. The framework supports voice-controlled computing
Agent-S is a multimodal AI agent and LLM desktop automation framework designed to control operating systems through graphical user interface interactions. It functions as a computer use interface, utilizing vision-language grounding to translate natural language goals into precise screen coordinates and system actions. The project differentiates itself by combining structured accessibility tree inspection with vision-based element localization. It manages cross-application workflows by mapping conceptual descriptions to physical pixels and simulating low-level keyboard and mouse events to mov
Qwen2.5-VL is an autoregressive multimodal transformer designed to process interleaved sequences of text and visual tokens. It integrates visual feature embeddings into a shared language model space to perform cross-modal reasoning and generate coherent responses or structured layout code. The project distinguishes itself through vision-language-action mapping, allowing it to perceive visual interfaces and translate that perception into actionable commands for operating digital screens and robotic hardware. It employs dynamic-resolution image encoding and temporal-frame video indexing to hand
Aligning Text and Embodied Environments for Interactive Learning Mohit Shridhar, Xingdi (Eric) Yuan, Marc-Alexandre Côté, Yonatan Bisk, Adam Trischler, Matthew Hausknecht ICLR 2021
We propose ZeroGUI, a fully automated online reinforcement learning framework that enables GUI agents to train and adapt in interactive environments at zero human cost.
The main features of opengvlab/zerogui are: GUI and Computer Agents, Reasoning Environments.
Projects with overlapping indexed features include: othersideai/self-operating-computer — This project is a computer control framework that uses multimodal vision models to simulate mouse and keyboard inputs… simular-ai/agent-s — Agent-S is a multimodal AI agent and LLM desktop automation framework designed to control operating systems through… qwenlm/qwen2.5-vl — Qwen2.5-VL is an autoregressive multimodal transformer designed to process interleaved sequences of text and visual… alfworld/alfworld — Aligning Text and Embodied Environments for Interactive Learning Mohit Shridhar, Xingdi (Eric) Yuan, Marc-Alexandre… bytedance/ui-tars-desktop — UI-TARS-desktop is a cross-platform desktop application designed to automate software interface interactions. It… bytedance/ui-tars — UI-TARS is an LLM GUI automation framework and multimodal action grounding system. It functions as a GUI agent…