This review provides a comprehensive analysis of instruction-based computer control agents (CCAs). These agents use natural language instructions to perform complex actions on computers and mobile devices, interacting with graphical user interfaces (GUIs) in a manner similar to human users. The shift from manually designed, specialized CCAs to leveraging foundation models like Large Language Models (LLMs) and Vision-Language Models (VLMs) is a key focus.
The review establishes a taxonomy for CCAs, analyzing them from three perspectives: the environment (computer systems), the interaction (observation and action spaces like screenshots, HTML, mouse/keyboard actions, and executable code), and the agent itself (learning and action principles). This framework allows for a comparative analysis of both specialized and foundation model-based CCAs.
A significant portion of the review focuses on how foundation models like LLMs and VLMs are transforming the development of CCAs. It explores how these models enable the creation of more capable and adaptable agents compared to their manually designed predecessors. The review highlights the advantages and limitations of this approach.
The review compares and contrasts specialized and foundation model-based CCAs, emphasizing how insights from specialized agents can inform the development of more robust foundation agents. It highlights the key differences in design, training, and capabilities.
The review surveys existing CCA datasets and evaluation methodologies, identifying current limitations and suggesting improvements for more rigorous evaluation of CCA performance. It emphasizes the importance of standardized benchmarks for fair comparison.
The review concludes by outlining the key challenges in deploying CCAs in real-world settings, and proposes future research directions to address these challenges and push the boundaries of the field. This includes enhancing robustness, addressing safety and security concerns, and improving human-computer interaction within the CCA framework.
The review delves into the practical considerations of deploying CCAs in real-world scenarios. This includes addressing issues related to robustness, reliability, security, and user experience. It also highlights the importance of ethical considerations in the development and deployment of such agents.
The review concludes by offering a glimpse into the future of CCA research and development. It explores the potential for creating more intelligent, adaptable, and user-friendly CCA agents capable of handling increasingly complex tasks and diverse computer environments. The integration of advanced computer vision techniques and natural language processing capabilities are highlighted as key areas for future development.
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