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技術アーキテクチャ図を示すCAAI具身智能白書2026年版の表紙
ResearchJune 21, 2026Embodied Global Team

CAAIが具身智能白書2026年版を発表:包括的な技術フレームワークが業界の成熟を示す

中国人工智能学会(CAAI)が2026年版具身智能白書を発表、これまでで最も包括的な技術フレームワークを提供。知覚、推論、操作、ナビゲーション、世界モデル、基盤モデルを網羅し、VLAからWAMへの移行を次のパラダイムシフトと位置付け。5つの主要産業分野をカバーし、具身智能の展開に関する安全性と倫理的ガバナンス基準を確立。

Reading in JA

The Chinese Association for Artificial Intelligence (CAAI) has released its authoritative Embodied Intelligence White Paper (2026 Edition), offering the most systematic technical assessment of the field to date. The document arrives at a pivotal moment when embodied AI is transitioning from laboratory research to industrial-scale deployment.

Key Technical Framework:

The white paper organizes embodied intelligence technology into three layers:

  • Foundation Layer: Embodied perception (multimodal fusion, active perception), embodied reasoning (LLM-driven task decomposition, code-as-policy), embodied manipulation (VLA models evolving to WAM), embodied navigation, and reinforcement learning
  • Advanced Layer: Human-robot interaction, swarm intelligence, world models, and embodied foundation models
  • Safety Layer: Comprehensive risk coverage including planning, navigation, manipulation, and interaction safety, addressing voice hijacking, GPS attacks, sensor attacks, hallucinations, and backdoors

Paradigm Shift Identified: The white paper identifies the transition from VLA (Vision-Language-Action) models to WAM (World-Action Models) as the next major paradigm shift. WAM models go beyond imitation learning by understanding physical causality, enabling robots to predict the consequences of their actions in the physical world.

Industry Verticals: Five major application domains are covered: lifestyle services (home management, retail, education), industrial manufacturing (flexible assembly, intelligent scheduling), agriculture (autonomous farming, precision agriculture), transportation (infrastructure inspection, autonomous driving, intelligent logistics), and energy (transmission inspection, substation operations, storage coordination).

Data Challenges Addressed: The white paper notes that real-robot data offers precision but at high cost; simulation data is efficient but suffers from sim-to-real gaps; and internet video data is abundant but lacks physical interaction information. The path forward lies in low-cost, portable, cross-embodiment data collection.

Future Outlook: The document forecasts that over the next decade, embodied intelligence will fundamentally reshape production and lifestyle patterns, becoming a key driver of 'new quality productive forces.' Key bottlenecks remain in data scale, generalization capability, reliability, and safety ethics governance.