Body Discovery of Embodied AI

• URL: http://arxiv.org/abs/2503.19941v1
• Date: Tue, 25 Mar 2025 09:21:10 GMT
• Title: Body Discovery of Embodied AI
• Authors: Zhe Sun, Pengfei Tian, Xiaozhu Hu, Xiaoyu Zhao, Huiying Li, Zhenliang Zhang,
• Abstract summary: "Body Discovery of Embodied AI" focuses on tasks of recognizing embodiments and summarizing neural signal functionality.<n>The challenge encompasses the precise definition of an AI body and the intricate task of identifying embodiments in dynamic environments.<n>We develop a simulator tailored for testing algorithms with virtual environments.
• Score: 14.90599757805173
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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