Semantic Intelligence: A Bio-Inspired Cognitive Framework for Embodied Agents

TL;DR

The paper addresses the gap between disembodied language models and embodied interaction by proposing the Semantic Intelligence-Driven Embodied (SIDE) framework, which endows agents with bio-inspired semantic intelligence. It combines a hierarchical semantic cognition architecture with a semantic-driven decision loop to form a closed perception–cognition–action cycle, regulated by metacognition and supported by working and long-term memory, with LLMs/VLMs/VLAs facilitating planning. Key contributions include the formalization of a three-dimensional semantic cognition stack, a unified semantic representation $\mathcal{K} = \mathcal{T} \otimes \mathcal{S} \otimes \mathcal{C}$, and an explicit decision loop using $\mathbf{O} = Bind(Attend(\mathbf{F_t}, \mathbf{F_s}, \mathbf{F_c}))$ to ground actions in semantics; applications in navigation and manipulation illustrate the framework's potential. This work lays a foundation for more flexible, interpretable, and robust embodied agents capable of context-aware reasoning in real-world settings.

Abstract

Recent advancements in Large Language Models (LLMs) have greatly enhanced natural language understanding and content generation. However, these models primarily operate in disembodied digital environments and lack interaction with the physical world. To address this limitation, Embodied Artificial Intelligence (EAI) has emerged, focusing on agents that can perceive and interact with their surroundings. Despite progress, current embodied agents face challenges in unstructured real-world environments due to insufficient semantic intelligence, which is critical for understanding and reasoning about complex tasks. This paper introduces the Semantic Intelligence-Driven Embodied (SIDE) agent framework, which integrates a hierarchical semantic cognition architecture with a semantic-driven decision-making process. This enables agents to reason about and interact with the physical world in a contextually adaptive manner. The framework is inspired by biological cognitive mechanisms and utilizes bio-inspired principles to design a semantic cognitive architecture that mimics how humans and animals integrate and process sensory information. We present this framework as a step toward developing more intelligent and versatile embodied agents.

Figures (4)

• Figure 1: Two scenarios demonstrating the necessity of semantic understanding in embodied agents.
• Figure 2: The framework of semantic intelligence-driven embodied agents.
• Figure 3: Three basic types of cognition make up semantic cognition.
• Figure 4: An example of semantic-driven decision process.