Neural Brain: A Neuroscience-inspired Framework for Embodied Agents (2505.07634v2)

Abstract: The rapid evolution of 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 LLMs, 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.

• The paper introduces the Neural Brain, a neuroscience-inspired framework integrating sensing, cognition, and memory to create adaptable, dynamic embodied agents.
• Core to the Neural Brain are systems for multimodal active sensing, a perception-cognition-action loop, and neuroplasticity-inspired memory, designed for real-world interaction.
• The framework aims to create highly adaptable autonomous agents by integrating neuroscience principles and leveraging energy-efficient neuromorphic hardware for real-time performance.

Neural Brain: A Neuroscience-Inspired Framework for Embodied Agents

The paper "Neural Brain: A Neuroscience-inspired Framework for Embodied Agents" explores the rapid evolution in AI as it progresses from static, data-driven models towards dynamic systems capable of real-world environmental interaction. It introduces the Neural Brain concept—a central intelligence system devised to enable embodied agents such as humanoid robots to navigate complex environments with human-like adaptability. The work is an overview of insights from neuroscience and AI, aiming to bridge the gap between static AI models and the dynamic adaptability required for real-world deployment, along with proposing a biologically inspired architecture encompassing sensing, perception-cognition-action dynamics, memory, and hardware/software integration.

Key Components of the Neural Brain

The framework for the Neural Brain revolves around integrating several cognitive capabilities:

1. Multimodal Active Sensing: This involves hierarchical sensing integration, where the system processes multimodal inputs such as vision, audition, tactile, and spatial awareness similar to how the human brain integrates sensory information. Active sensing strategies enhance sensory perception dynamically in response to the environment and task priorities. Adaptive calibration ensures the stability of sensory data through self-calibrating mechanisms, emulating human sensory adaptation.
2. Perception-Cognition-Action Loop: This loop forms the backbone of the Neural Brain, mirroring the closed-loop processes seen in the human brain. The integration of perception and cognition allows systems to refine actions via real-time feedback, cognitive reasoning, and inference, enabling higher-level cognitive tasks. This approach supports anticipatory responses and decision-making based on predictive models and past experiences.
3. Neuroplasticity-Driven Memory Systems: Inspired by human memory mechanisms, the Neural Brain integrates short-term and long-term memory to ensure adaptive learning and retention of critical information. It focuses on hierarchical memory architectures, adaptive resolution based on task demands, and context-aware memory retrieval, aiming for efficient memory consolidation and recall similar to neural plasticity seen in humans.
4. Energy-Efficient Neuromorphic Architecture: To optimize real-time operations in power-constrained environments, the framework emphasizes the use of Spiking Neural Networks (SNNs) and specialized neuromorphic hardware. This component ensures efficient, event-driven processing, enabling embodied agents to perform real-time tasks while maintaining a low energy footprint.

Implications and Future Directions

This foundational framework posits several implications, both practical and theoretical, for the advancement of AI. Practically, it suggests a path forward for developing autonomous, intelligent systems with capabilities akin to biological organisms. The theoretical implications lie in advancing our understanding of how principles of human cognition and neural structures can be effectively translated into artificial systems.

Future AI developments will benefit significantly from further exploration in:

• Enhancing the efficacy and scope of multimodal-sensing systems to encompass broader sensory inputs.
• Developing more sophisticated active perception approaches that learn to focus sensory and computational resources dynamically.
• Refining memory systems that not only store knowledge effectively but also leverage it for proactive decision-making through continuous learning.
• Integrating cutting-edge neuromorphic technologies capable of supporting advanced neural simulations with resource constraints typical of edge computing scenarios.

Overall, this paper underlines an ambitious trajectory for embodied AI, where the integration of neuroscience-inspired design and AI engineering could lead to breakthroughs in creating systems with greater adaptability and intelligence, potentially transforming human-robot interaction and autonomous functionalities in complex, real-world settings.