Self Model for Embodied Artificial Intelligence

This paper presents a systematic formulation of the self model for embodied artificial intelligence (AI), aiming to provide the missing internal representation that enables an agent to understand its own body, capabilities, memories, and decision processes. Unlike existing approaches that address isolated aspects such as perception, prediction, or skill adaptation, we propose a unified computational framework that integrates body schema, forward and inverse models, perceptual memory mechanisms, and agency. This framework captures how an embodied agent represents its physical structure, predicts the consequences of its actions, selects policies, and accumulates experiences to form a coherent sense of self. We further introduce a six-level hierarchy (L0–L5) that characterizes the developmental stages of self model from non-self representation to full self-awareness, providing the first operational taxonomy for evaluating self-awareness in embodied AI systems. Then, a practical implementation is proposed and validated on embodied navigation and manipulation tasks. Experimental results demonstrate the effectiveness of the self model and its components, including self-perception, self-memory, self-prediction, and self-decision. Overall, this work proposes the concept and instantiation of the self model in embodied AI and discusses future directions of the self model. Related video demonstrations can be accessed from https://taoshida11.github.io/Selfmodel/.