自然杂志 ›› 2025, Vol. 47 ›› Issue (6): 437-445.doi: 10.3969/j.issn.0253-9608.2025.06.003

所属专题: 智能机器人

• 特约专稿 • 上一篇    下一篇

机器意识与具身智能

张晓林,王开放
  

  1. ①上海大学 仿生视觉与类脑智能研究所,上海 200444;②安徽爱观视觉科技有限公司,上海 200233
  • 出版日期:2025-12-25 发布日期:2025-12-17

Machine consciousness and embodied intelligence

ZHANG Xiaolin, WANG Kaifang
  

  1. ① Bio-Vision & Brian-inspired Intelligence Lab, Shanghai University, Shanghai 200444, China; ② Anhui Eyevolution Technology Co., Ltd., Shanghai 200233, China
  • Online:2025-12-25 Published:2025-12-17

摘要:

具身智能指赋予人工智能以物理身体来增强其实践交互能力。然而研究者长期回避“机器意识”的讨论以避免争议。随着大语言模型(如ChatGPT和DeepSeek等)展现出对环境与身体交互的渴求,“机器意识”已成为不可回避的前沿课题。本文认为机器意识——即人工智能对自身内部状态及外部环境的觉知能力——是自主适应决策的关键。文章探讨了机器意识与具身智能的内在联系,并借鉴生物学中关于意识模块化和无意识过程的研究,提出类脑的“意识空间”框架,用于融合多模态感知,进行内部模拟与情境推理。本文进一步阐述了意识对决策生成的支撑作用以及决策执行对意识价值的验证,最后展望了未来在多模态统一意识空间建模、主动推断式学习以及机器伦理控制等方向的研究前景。

关键词:

Abstract:

Embodied intelligence refers to AI systems equipped with a physical body to enhance real-world interaction capabilities. However, researchers have long avoided the topic of “machine consciousness” to sidestep controversy. With the advent of large language models (e.g., ChatGPT and DeepSeek) showing a strong desire for environmental and bodily interaction, machine consciousness becomes an unavoidable frontier issue. This article posits that machine consciousness, an AI system’s awareness of its own internal state and external environment, is crucial for autonomous adaptive decision-making. We explore the intrinsic relationship between machine consciousness and embodied intelligence, drawing on biological insights into the modular nature of consciousness and unconscious processes. A brain-inspired “consciousness space” framework is proposed to integrate multi-modal sensory information for internal simulation and contextual reasoning. We further discuss how consciousness supports the formation of decisions and how the execution of decisions validates the value of conscious processing. Finally, the paper outlines future research directions, including unified multi-modal consciousness-space modeling, active inference-based learning, and considerations of machine ethics and control boundaries.