2024 AnInteractiveAgentFoundationMod
- (Durante et al., 2024) ⇒ Zane Durante, Bidipta Sarkar, Ran Gong, Rohan Taori, Yusuke Noda, Paul Tang, Ehsan Adeli, Shrinidhi Kowshika Lakshmikanth, Kevin Schulman, Arnold Milstein, Demetri Terzopoulos, Ade Famoti, Noboru Kuno, Ashley Llorens, Hoi Vo, Katsu Ikeuchi, Li Fei-Fei, Jianfeng Gao, Naoki Wake, and Qiuyuan Huang. (2024). “An Interactive Agent Foundation Model.” doi:10.48550/arXiv.2402.05929
Subject Headings: Multi-Task Agent Training, Multimodal Learning, Generalist Action-Taking Multimodal System, Cross-Domain Applicability of AI Model, Interactive Agent AI Model.
Notes
- It introduces the Interactive Agent Foundation Model as a versatile framework for AI agents in domains like Robotics, Gaming AI, and Healthcare, enabling the development of adaptable and domain-agnostic AI models.
- It employs a novel multi-task agent training approach, merging various pre-training strategies to enhance the versatility and efficiency of AI agents across diverse applied domains.
- It demonstrates the model's effectiveness in areas such as Robotics, Gaming AI, and Healthcare, showcasing its adaptability and potential to enhance operations and user experiences.
- It leverages a broad range of data sources for multimodal and multi-task learning, allowing agents to understand better and act within complex environments.
- It aims to develop generalist action-taking multimodal systems by integrating text, visual data, and actions in the pre-training phase, thus enhancing their applicability in real-world scenarios.
- It assesses the model's performance using robotics and gaming data to show its effective transfer to healthcare tasks, highlighting its cross-domain applicability.
- It discusses the societal implications and potential applications of interactive agent AI models, underlining their transformative potential across various application fields.
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Abstract
The development of artificial intelligence systems is transitioning from creating static, task-specific models to dynamic, agent-based systems capable of performing well in a wide range of applications. We propose an Interactive Agent Foundation Model that uses a novel multi-task agent training paradigm for training AI agents across a wide range of domains, datasets, and tasks. Our training paradigm unifies diverse pre-training strategies, including visual masked auto-encoders, language modeling, and next-action prediction, enabling a versatile and adaptable AI framework. We demonstrate the performance of our framework across three separate domains - - Robotics, Gaming AI, and Healthcare. Our model demonstrates its ability to generate meaningful and contextually relevant outputs in each area. The strength of our approach lies in its generality, leveraging a variety of data sources such as robotics sequences, gameplay data, large-scale video datasets, and textual information for effective multimodal and multi-task learning. Our approach provides a promising avenue for developing generalist, action-taking, multimodal systems.
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