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凯捷:2026物理AI:推动人机协作迈向新阶段研究报告(中译版)(140页).pdf

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1、Make it real.Physical AI Taking human-robot collaboration to the next levelTable of contents2Capgemini Research Institute 2026Physical AI:Taking human-robot collaboration to the next levelWho should read this report and why04Executive summary0824Why physical AI is at an inflection point36Physical AI

2、 is a game-changer for industry56The growing imperative to adopt physical AI3Capgemini Research Institute 2026Physical AI:Taking human-robot collaboration to the next levelConclusion 104Research methodology10570Scaling physical AI goes beyond technology,spanning safety,cybersecurity,regulation,and o

3、perations78Humanoids set the stage for general-purpose robotics96Recommendations:Accelerating the physical AI revolutionWho should read this report and why?This report is intended for senior executives shaping their organizations approach to robotics and automation.It examines how physical AI is tra

4、nsforming robotics from the capabilities it enables to the value it unlocks,the timelines for adoption,and the barriers that must be addressed to scale deployments safely and effectively.It will be particularly relevant to technology and innovation leaders(including chief technology officers,chief i

5、nnovation officers,chief digital officers,and heads of AI or robotics),as well as manufacturing,supply chain,and logistics leaders responsible for robotics strategy and deployment.As robotics expands into consumer-facing and service environments such as healthcare,retail,hospitality,and entertainmen

6、t the report is also relevant to chief product officers,product strategists,and experience design leaders who are shaping interactions between people and intelligent machines.In addition,the report provides practical guidance for CROs and safety or regulatory leaders preparing their organizations fo

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1. **物理AI定义与价值**:物理AI使机器人具备感知、推理和自主行动能力,从固定程序转向适应非结构化环境,67%高管认为其将颠覆行业,64%视其为关键竞争力。 2. **应用场景**:已应用于物流(如FedEx自动装货)、制造(Foxconn动态组装)、医疗(Wandercraft外骨骼)等领域,解决劳动力短缺(74%)、高成本(69%)等问题。 3. **技术驱动**:多模态基础模型、仿真训练、AI-机器人-数据飞轮推动发展,硬件成本下降(如机器人成本降30倍)和RaaS模式降低 adoption 障碍。 4. **挑战与建议**:需解决可靠性、数据稀缺、安全及监管问题;建议从“脏险累”任务入手,设计协作流程,构建可扩展平台。 5. **人形机器人**:67%高管认为其将长期变革行业,但规模化部署需7年,仅30%认为3-5年内可行,技术成熟度、成本及公众接受度(62%)为主要障碍。
机器人新纪元? 物理AI是什么? 人机协作未来?
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