Summary of Cradle: Empowering Foundation Agents Towards General Computer Control, by Weihao Tan et al.
Cradle: Empowering Foundation Agents Towards General Computer Control
by Weihao Tan, Wentao Zhang, Xinrun Xu, Haochong Xia, Ziluo Ding, Boyu Li, Bohan Zhou, Junpeng Yue, Jiechuan Jiang, Yewen Li, Ruyi An, Molei Qin, Chuqiao Zong, Longtao Zheng, Yujie Wu, Xiaoqiang Chai, Yifei Bi, Tianbao Xie, Pengjie Gu, Xiyun Li, Ceyao Zhang, Long Tian, Chaojie Wang, Xinrun Wang, Börje F. Karlsson, Bo An, Shuicheng Yan, Zongqing Lu
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel General Computer Control (GCC) setting is proposed to restrict foundation agents to interact with software through a standardized interface using screenshots as input and keyboard/mouse actions as output. The Cradle framework, powered by LMM, enables low-level control after high-level planning, allowing for interaction with any software and completion of complex tasks without relying on built-in APIs. Experimental results show remarkable generalizability and impressive performance across various commercial video games, software applications, and benchmarks, including the completion of long-horizon missions in Red Dead Redemption 2 and other tasks in popular games like Cities: Skylines and Stardew Valley. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary Foundation agents are designed to interact with specific environments. However, they struggle to generalize across different scenarios due to manually designed observation and action spaces. To address this issue, a new General Computer Control (GCC) setting is introduced. It allows agents to interact with software through screenshots as input and keyboard/mouse actions as output. The Cradle framework uses LMM to enable low-level control after high-level planning. This enables agents to complete complex tasks without relying on built-in APIs. |