Neural Brain: A Neuroscience-inspired Framework for Embodied Agents

2025-05-12

AI Summary

Current AI systems—particularly large language models—lack real-time physical interaction capabilities and adaptive behavior in unstructured environments, hindering the advancement of embodied intelligence. Method: This paper introduces the “Neural Brain” framework—the first brain-inspired central intelligence architecture specifically designed for embodied agents—aiming to bridge the gap between static AI and human-level dynamic adaptability. It systematically defines four core components: (i) brain-like multimodal active perception, (ii) neuroplastic memory, (iii) integrated sensing–cognition–action architecture, and (iv) neuromorphic software–hardware co-design. By synergizing multimodal sensing, joint modeling, and hardware optimization, the framework enables closed-loop perception–cognition–action and online environmental adaptation. Contribution: The work identifies critical capability gaps in embodied intelligence, establishes a unified theoretical and technical framework, and outlines a concrete pathway toward general-purpose embodied intelligence.

Technology Category

• Intelligent Robots: Embodied AI
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

The rapid evolution of artificial intelligence (AI) has shifted from static, data-driven models to dynamic systems capable of perceiving and interacting with real-world environments. Despite advancements in pattern recognition and symbolic reasoning, current AI systems, such as large language models, remain disembodied, unable to physically engage with the world. This limitation has driven the rise of embodied AI, where autonomous agents, such as humanoid robots, must navigate and manipulate unstructured environments with human-like adaptability. At the core of this challenge lies the concept of Neural Brain, a central intelligence system designed to drive embodied agents with human-like adaptability. A Neural Brain must seamlessly integrate multimodal sensing and perception with cognitive capabilities. Achieving this also requires an adaptive memory system and energy-efficient hardware-software co-design, enabling real-time action in dynamic environments. This paper introduces a unified framework for the Neural Brain of embodied agents, addressing two fundamental challenges: (1) defining the core components of Neural Brain and (2) bridging the gap between static AI models and the dynamic adaptability required for real-world deployment. To this end, we propose a biologically inspired architecture that integrates multimodal active sensing, perception-cognition-action function, neuroplasticity-based memory storage and updating, and neuromorphic hardware/software optimization. Furthermore, we also review the latest research on embodied agents across these four aspects and analyze the gap between current AI systems and human intelligence. By synthesizing insights from neuroscience, we outline a roadmap towards the development of generalizable, autonomous agents capable of human-level intelligence in real-world scenarios.

Problem

• Developing a neuroscience-inspired framework for embodied AI agents
• Integrating multimodal sensing with cognitive capabilities for adaptability
• Bridging static AI models and dynamic real-world deployment needs

Innovation

• Biologically inspired architecture integrating multimodal sensing
• Neuroplasticity-based memory storage and updating system
• Neuromorphic hardware-software co-design for energy efficiency