身体発見：身体性AIの新たな課題
Body Discovery of Embodied AI
身体性AIが多様な身体を認識し適応するための「身体発見」課題を提唱し、因果推論手法と専用シミュレータでその有効性を検証した。
著者: Zhe Sun, Pengfei Tian, Xiaozhu Hu, Xiaoyu Zhao, Huiying Li, Zhenliang Zhang
分類: cs.RO, cs.AI, cs.NE
原文アブストラクト
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.
