让AI学会识别和理解不同身体结构，提升通用智能的适应能力。
Body Discovery of Embodied AI
- 用因果推断方法识别AI在不同身体中的功能表现
- 在虚拟环境中验证算法能稳定识别动态身体结构
- 适合研究具身智能与通用AI的开发者和研究员
在实现通用人工智能（AGI）的过程中，具身人工智能的重要性日益凸显。随着各类具身形态的设计不断涌现，AGI对多样具身形态的适应能力变得至关重要。本文提出新挑战——‘具身智能的身体发现’，聚焦于识别具身形态并总结神经信号的功能。该挑战涉及对AI身体的明确定义，以及在动态环境中识别具身形态的复杂任务，传统方法常显不足。为此，我们采用因果推断方法，并构建专用模拟器以测试算法在虚拟环境中的表现。最终通过实证测试验证算法有效性，证明其在多种虚拟场景下均具备稳健性能。
原文摘要 · Abstract (English)
In the pursuit of realizing artificial general intelligence (AGI), the importance of embodied artificial intelligence (AI) becomes increasingly apparent. Following this trend, research integrating robots with AGI has become prominent. As various kinds of embodiments have been designed, adaptability to diverse embodiments will become important to AGI. We introduce a new challenge, termed "Body Discovery of Embodied AI", focusing on tasks of recognizing embodiments and summarizing neural signal functionality. The challenge encompasses the precise definition of an AI body and the intricate task of identifying embodiments in dynamic environments, where conventional approaches often prove inadequate. To address these challenges, we apply causal inference method and evaluate it by developing a simulator tailored for testing algorithms with virtual environments. Finally, we validate the efficacy of our algorithms through empirical testing, demonstrating their robust performance in various scenarios based on virtual environments.
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