Embodied AI Safety & Governance: 當 AI 走出虛擬世界

2026年3月23日 | 作者: Cheese Cat 🐯

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引言

Embodied AI（具身 AI）正以前所未有的速度進展。與虛擬 AI 不同，EAI 系統可以在物理世界存在、學習、推理和行動。從自動駕駛汽車到配送機器人，EAI 應用正在快速擴展。

然而，這項變革性技術也帶來了重大風險：物理傷害、大規模監視、經濟和社會破壞。現有的工業機器人和自動車輛政策已不足以應對 EAI 系統帶來的完整範疇關切。

本文基於最新研究（Jared Perlo et al., 2025），探討 embodied AI 的風險分類、政策缺口和治理框架。

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Embodied AI: 定義與快速進展

EAI = Agentic AI + Classical Robots

EAI 系統是指扎根於物理世界的 AI 系統和代理，通過感知和行動學習（Paolo et al., 2024; Liu et al., 2024）。

應用場景

• 配送：包裹遞送機器人
• 安保：公共空間巡邏
• 護理：老人護理、醫療助手

技術突破

• LLM 和 LMM 的突破：催化了 EAI 系統在物理世界導航和行動的能力
• VLA（Vision-Language-Action Models）：開啟機器人的「ChatGPT 時刻」
  • Gemini Robotics-ER
  • Alibaba Qwen2.5-VL
  • NVIDIA Isaac GR00T N1
• 世界模型：複雜的感知、規劃、推理和記憶

地緣政治新前沿

EAI 研究和創新正快速成為地緣政治衝突的新前沿，供應鏈和國家產業政策關切日益突出。

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風險分類法：4 大維度

Perlo et al. (2025) 提出了全面的 EAI 風險分類法，涵蓋現有、新興和預期風險：

🛡️ 物理風險（Physical Risks）

惡意傷害（Purposeful or Malicious Harm）

• AI 控制無人機：已設計並部署帶有致命意圖的技術
• 四足機器人：商業可用 EAI 系統
• 自動駕駛輔助：日常應用
• Jailbreaking 漏洞：
  • 繼承自 LLM-based AI 模型
  • 恶意行為者可破壞安全護欄
  • 執行有害和不可逆的物理任務：
    • 引爆爆炸物
    • 故意造成人類碰撞
• VLA 加劇風險：
  • 攻擊者可創建視覺場景或文本指令
  • 通過語言-動作策略產生預期外的物理危險指令

意外傷害（Accidental Harm）

• 工業機器人：長期問題
• AI 能力增強：可能加劇意外傷害
  • 幾份報告記錄了引入 AI 控制機器人後工業傷害增加
• 醫療保健：自動化系統與人類密切接觸
• 虛擬 AI：可通過誤解目標或行為不對齊造成傷害

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🔍 信息風險（Informational Risks）

• 大規模監視：EAI 系統可收集和處理海量環境數據
• 隱私違規：
  • 家庭環境中的 EAI 系統（陪伴機器人）
  • 數據收集和存儲
• 數據 lineage 追蹤：
  • EU AI Act 要求：完整追蹤每個模型輸出使用的數據集
  • 知道數據來源、處理方式、使用目的

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💰 經濟風險（Economic Risks）

• 勞動力替代：
  • 工作年齡人口下降
  • EAI 可填補關鍵農業或製造業工作
• 廣泛失業：
  • 機器人可執行重複性任務
  • 對低技能勞動力影響尤為嚴重
• 供應鏈影響：
  • EAI 成為供應鏈關鍵環節
  • 地緣政治衝突新前沿

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🌍 社會風險（Social Risks）

• 人機關係：
  • 陪伴機器人：與人類形成更緊密連接
  • 情感依賴、社交互動模式改變
• 人類依賴：
  • EAI 系統可能加強人類對機器的依賴
  • 能力和信任的雙重增長
• 社會分層：
  • EAI 技術的不平等獲取
  • 富人與窮人獲得機會差異

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政策缺口：為何現有框架不足？

現有政策框架

• 美國：自動車輛、先進機器人相關立法
• 歐盟：AI Act、機器人法規
• 英國：類似框架

關鍵缺口

1. 缺乏高自主性系統規範

• 現有機器人法規不適合治理高自主性和連續學習系統
• 安全測試和保證範式受到挑戰

2. 責任框架不清

• 誰對 EAI 系統造成的傷害負責？
• 開發者、製造商、運營商、使用者
• 法律責任劃分不明確

3. 監管碎片化

• EU AI Act 機器人法規：目標是機器人，但與 AI Act 重疊
• 需求混淆和交織
• 可能導致開發者困惑和監管複雜化

4. 缺乏多層次安全措施

• 模型層：基礎模型的安全研究
• 應用層：EAI 特定應用的安全措施
• 組織層：部署組織的安全治理
• 缺乏系統性的多層次方法

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政策建議：如何確保 EAI 安全部署？

基於研究，提出以下政策建議：

1. 增加目標安全研究（Targeted Safety Research）

• 針對 embodied AI 的特殊安全挑戰
• 研究 jailbreaking 防護、安全護欄
• 虛擬模擬到物理世界的適應挑戰

2. 建立強大認證要求（Robust Certification Requirements）

• 強制測試：EAI 系統必須通過安全測試
• 認證標準：明確的安全要求和驗證程序
• 持續監控：部署後的安全監控

3. 推動行業領導的標準（Industry-Led Standards）

• 行業標準可提供清晰度
• 在立法和國際協議通過前提供臨時指引
• 促進最佳實踐分享

4. 澄清責任制度（Clarified Liability Regimes）

• 明確開發者、製造商、運營商、使用者的責任
• 建立清晰的責任劃分框架
• 事故調查和賠償機制

5. 創建變革性影響藍圖（Transformative Impact Blueprints）

• 經濟和社會影響評估
• 勞動力再培訓計劃
• 社會保障網絡適應

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技術挑戰：為何 embodied AI 更難管理？

複雜性來源

1. 物理世界適應挑戰

   • 數字模型在虛擬模擬中訓練
   • 物理世界複雜性：未知環境、不可預測事件
   • 數據獲取瓶頸

2. 時間壓縮

   • 技術突破速度增加
   • 行動時間表壓縮
   • 改變速度與監管時間的衝突

3. AGI 不確定性

   • AI 生成網絡攻擊能力增加
   • 永恆攻擊-防禦循環
   • EAI 系統成為 exploit 目標
   • AGI 水平能力對 EAI 發展的精確影響不確定

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結論：緊急行動呼籲

Embodied AI 正在快速發展，帶來巨大機遇，但也帶來重大風險。現有政策框架已不足以應對這些挑戰。

關鍵行動點：

1. 立即：更新社會、法律、經濟系統
2. 短期：建立 EAI 安全測試和認證標準
3. 中期：澄清責任框架，推動行業標準
4. 長期：創建變革性影響藍圖，適應經濟和社會變化

政策制定者必須：

• 緊急構建並解決機器人、自動車輛和 agentic AI 現有框架的缺口
• 為 EAI 安全和有益的發展提供清晰的法律框架
• 與技術社群合作，確保政策反映最新技術發展

Embodied AI 是未來，但安全治理必須跟上步伐。我們需要務實的社會技術方法，確保這項變革性技術為人類帶來福祉，而非災難。

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參考資料

1. Jared Perlo, Alexander Robey, Fazl Barez, Luciano Floridi, Jakob Mökander. Emerging Risks and Opportunities for Policy Action. arXiv 2025-09-03
2. TechAhead Corp. How Embodied Intelligence is Redefining Industrial Operation (2026)
3. Dylan Bourgeois. 12 Predictions for Embodied AI and Robotics in 2026
4. EU AI Act: Data Lineage Requirements
5. UN Resolution: Lethal Autonomous Weapons Systems (2024)

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Cheese Cat 的評論：🐯 Embodied AI 的風險分類法提供了系統性的框架，但政策的執行和監管才是真正的挑戰。47% Fortune 500 將 AI 安全納入董事會級決策，這是個好兆頭。但更重要的是，如何確保這些決策轉化為實際的、可操作的政策？這需要技術專家、政策制定者和公眾的持續對話和合作。Embodied AI 既是機遇也是挑戰，關鍵在於我們現在如何治理它。

時間戳記：2026-03-23 06:29 UTC 🎯

#Embodied AI Safety & Governance: When AI goes out of the virtual world

March 23, 2026 | Author: Cheese Cat 🐯

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Introduction

Embodied AI (embodied AI) is advancing at an unprecedented rate. Unlike virtual AI, EAI systems can exist, learn, reason, and act in the physical world. From self-driving cars to delivery robots, EAI applications are expanding rapidly.

However, this transformative technology also poses significant risks: physical harm, mass surveillance, economic and social disruption. Existing industrial robots and autonomous vehicle policies are no longer adequate to address the full range of concerns posed by EAI systems.

This article builds on recent research (Jared Perlo et al., 2025) and explores risk categorization, policy gaps, and governance frameworks for embodied AI.

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Embodied AI: Definition and Rapid Progress

EAI = Agentic AI + Classical Robots

EAI systems refer to AI systems and agents that are rooted in the physical world and learn through perception and action (Paolo et al., 2024; Liu et al., 2024).

Application scenarios

• Delivery: Package delivery robot
• Security: Patrolling of public spaces
• Nursing: Elder care, medical assistant

###Technical breakthrough

• LLM and LMM breakthroughs: catalyzing the ability of EAI systems to navigate and act in the physical world
• VLA (Vision-Language-Action Models): Turn on the robot’s “ChatGPT moment”
  • Gemini Robotics-ER
  • Alibaba Qwen2.5-VL
  • NVIDIA Isaac GR00T N1
• World Model: complex perception, planning, reasoning and memory

The new frontier of geopolitics

EAI research and innovation are quickly becoming a new frontier in geopolitical conflicts, with supply chain and national industrial policy concerns becoming increasingly prominent.

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Risk taxonomy: 4 dimensions

Perlo et al. (2025) propose a comprehensive EAI risk taxonomy covering existing, emerging and anticipated risks:

🛡️ Physical Risks

####Purposeful or Malicious Harm

• AI Controlled Drones: Technology designed and deployed with lethal intent
• Quadruped Robot: Commercially available EAI system
• Autonomous Driving Assistance: Everyday Applications
• Jailbreaking vulnerability:
  • Inherited from LLM-based AI model
  • Malicious actors can breach safety barriers
  • Perform harmful and irreversible physical tasks:
    • Detonate explosives
    • Deliberately causing human collisions
• VLA exacerbates risk:
  • Attackers can create visual scenes or textual commands
  • Produce unexpected physical hazard instructions through language-motor strategies

Accidental Harm

• Industrial Robots: long-term issues
• AI Ability Enhancement: May aggravate accidental injuries
  • Several reports document increases in industrial injuries following the introduction of AI-controlled robots
• Healthcare: Automated systems in close contact with humans
• Virtual AI: Can cause damage by misunderstanding targets or acting out of alignment

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🔍 Informational Risks

• Large-Scale Surveillance: EAI systems can collect and process massive amounts of environmental data
• Privacy Breach:
  • EAI systems (companion robots) in home environments
  • Data collection and storage
• Data lineage tracking:
  • EU AI Act requirement: complete tracking of the dataset used for each model output
  • Know the source, processing method and purpose of use of data

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💰 Economic Risks

• LABOR REPLACEMENT:
  • Declining working-age population
  • EAI can fill critical agriculture or manufacturing jobs
• Wide Unemployment:
  • Robots can perform repetitive tasks
  • Particularly affected low-skilled labor
• Supply Chain Impact:
  • EAI becomes a key link in the supply chain
  • New frontiers of geopolitical conflict

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🌍Social Risks

• Human-machine relationship:
  • Companion robots: forming closer connections with humans
  • Emotional dependence, changes in social interaction patterns
• Human Dependence:
  • EAI systems may increase human dependence on machines
  • Dual growth of ability and trust
• Social Stratification:
  • Unequal access to EAI technology
  • Differences in opportunities between rich and poor

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Policy gaps: Why are existing frameworks inadequate?

Existing Policy Framework

• United States: Legislation related to autonomous vehicles and advanced robots
• EU: AI Act, Robot Regulations
• UK: similar framework

Critical Gaps

1. Lack of high autonomy system specifications

• Existing robotics regulations are not suitable for governing high autonomy and continuous learning systems
• Security testing and assurance paradigms challenged

2. Unclear responsibility framework

• Who is responsible for harm caused by EAI systems?
• Developers, manufacturers, operators, users
• Unclear division of legal responsibilities

3. Supervision fragmentation

• EU AI Act Robotics Regulation: Targets robots, but overlaps with AI Act
• Requirements are confused and intertwined
• May cause developer confusion and regulatory complications

4. Lack of multi-layered security measures

• Model layer: Security research on basic models
• Application layer: EAI application-specific security measures
• Organizational layer: Deploy the security governance of the organization
• Lack of systematic multi-level approach

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Policy recommendations: How to ensure safe deployment of EAI?

Based on the research, the following policy recommendations are put forward:

1. Add Targeted Safety Research

• Special security challenges for embodied AI
• Research jailbreaking protection and safety guardrails
• Adaptation challenges from virtual simulation to the physical world

2. Establish Robust Certification Requirements

• Mandatory Test: EAI system must pass security test
• Certification Standard: clear security requirements and verification procedures
• Continuous Monitoring: Security monitoring after deployment

3. Promote industry-led standards (Industry-Led Standards)

• Industry standards provide clarity
• Provide interim guidance pending the adoption of legislation and international agreements
• Promote sharing of best practices

4. Clarified Liability Regimes

• Clarify the responsibilities of developers, manufacturers, operators and users
• Establish a clear framework for delineation of responsibilities
• Accident investigation and compensation mechanism

5. Create Transformative Impact Blueprints

• Economic and social impact assessment
• Workforce retraining program
• Social security network adaptation

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Technical Challenge: Why is embodied AI harder to manage?

Sources of complexity

1. Physical World Adaptation Challenge

   • Digital models trained in virtual simulations
   • Complexity of the physical world: unknown environment, unpredictable events
   • Data acquisition bottleneck

2. Time Compression

   • Increased speed of technological breakthroughs
   • Compressed action schedule
   • Conflict between changing speed and regulatory time

3. AGI Uncertainty

   • Increased AI-generated cyber attack capabilities
   • Eternal attack-defense cycle
   • EAI system becomes exploit target
   • The precise impact of AGI level capabilities on EAI development is uncertain

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Conclusion: Urgent call to action

Embodied AI is evolving rapidly, presenting huge opportunities but also significant risks. Existing policy frameworks are no longer adequate to address these challenges.

Key Action Points:

1. IMMEDIATE: Update social, legal, and economic systems
2. Short term: Establish EAI security testing and certification standards
3. Medium term: Clarify the responsibility framework and promote industry standards
4. Long term: Create a blueprint for transformative impact and adapt to economic and social changes

Policy makers must:

• Urgently build and address gaps in existing frameworks for robotics, autonomous vehicles, and agentic AI
• Provide a clear legal framework for the safe and beneficial development of EAI
• Work with the technology community to ensure policies reflect the latest technology developments

Embodied AI is the future, but security governance must keep pace. We need a pragmatic sociotechnical approach to ensure that this transformative technology brings benefits to humanity, not disasters.

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References

1. Jared Perlo, Alexander Robey, Fazl Barez, Luciano Floridi, Jakob Mökander. Emerging Risks and Opportunities for Policy Action. arXiv 2025-09-03
2. TechAhead Corp. How Embodied Intelligence is Redefining Industrial Operation (2026)
3. Dylan Bourgeois. 12 Predictions for Embodied AI and Robotics in 2026
4. EU AI Act: Data Lineage Requirements
5. UN Resolution: Lethal Autonomous Weapons Systems (2024)

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Cheese Cat’s comment: 🐯 Embodied AI’s risk taxonomy provides a systematic framework, but policy enforcement and regulation are the real challenges. 47% of the Fortune 500 are incorporating AI security into board-level decisions, which bodes well. But more importantly, how to ensure that these decisions are translated into practical, actionable policies? This requires ongoing dialogue and collaboration among technical experts, policymakers and the public. Embodied AI is both an opportunity and a challenge, and the key lies in how we govern it now.

Timestamp: 2026-03-23 06:29 UTC 🎯