City-Level AI Services: From Pilot Programs to Routine Operations, Robots in Real-World Operations and Scaled Implementation | 2026 AI Partner · Beijing Yizhuang AI+ Industry Conference 城市级AI服务：从试点到常态化，机器人的实景作战与规模化落地| 2026AI Partner·北京亦庄AI+产业大会

The Core Challenge: Data as the Bottleneck for Embodied Intelligence

The article presents a pivotal argument: the primary barrier to advancing embodied intelligence is not algorithmic innovation but the lack of massive, real-world data. Unlike Large Language Models (LLMs) that train on abundant text data, training robots for complex physical tasks requires enormous volumes of interaction data from dynamic, real-world environments. The article draws a contrast with autonomous driving (Robotaxi), noting that while companies like Tesla leverage millions of vehicles for data collection, no analogous, mass-market “embodied intelligent terminal” exists for robotics. This creates a fundamental chicken-and-egg problem: without large-scale deployment, there’s no data; without data, the intelligence cannot evolve.

The Strategic Solution: “Fight to Train” and the World Model

KuaWhat’s strategic response is the “fight to train” model. This means moving beyond controlled testing grounds and deploying robots directly into real-world, revenue-generating services (like street cleaning and shuttle operations). Each robot becomes a mobile data collector as it navigates complex, unstructured environments. The operational data—on navigation, object interaction, and task execution—feeds back to refine and scale the AI models.

This approach is powered by a technological shift towards World Models. As explained in the speech by COO Li Kehong, 2023 marked a watershed moment. Newer World Models (like KuaWhat’s CooWAIM) are fundamentally different from previous modular robotics architectures. They are built on generative AI and are designed to simulate and predict physical world outcomes. By understanding environmental observations, they can forecast future actions and incorporate causal physics into decision-making. This creates a more robust and adaptable intelligence for robots.

Architecture and Application: The “One Brain, Multiple Forms” Paradigm

KuaWhat’s CooWAIM model uses a dual-system architecture:

1. The Intuitive Action System: Provides real-time, vision-based reasoning for immediate safety and efficiency (e.g., avoiding a sudden obstacle).
2. The Long-term Task Reasoning System: Handles global planning, semantic understanding, and complex task execution (e.g., planning a sanitation route).

These systems jointly enable two core capability domains:

• Drive (Full-domain Mobility): Allowing robots to navigate diverse terrains, from main roads to cluttered pedestrian sidewalks and indoor spaces.
• Work (Multi-joint Collaborative Operation): Integrating complex actuators like cleaning brushes and robotic arms, moving beyond simple pick-and-place to tightly coupled mobility and manipulation tasks.

This “one brain, multiple forms” architecture allows the same core AI to power different robot bodies—sanitation robots, autonomous buses, and delivery bots—across five key scenarios: sanitation, transportation, instant delivery, property management, and home services.

Economic Logic and the Path to Scale

A critical insight from the article is that technological capability must align with economic viability for successful scaling. KuaWhat emphasizes “economic rhythm”—deploying robots where the technology, product, and business models are mature enough to be profitable.

The progression is deliberately staged:

1. Start with open, high-volume scenarios (like sanitation): Achieve scale (e.g., 10,000 units) in the “Drive” domain first. Mastering unstructured mobility in complex city environments generates foundational data and proves economic value. Saving 20% operational time, for example, directly translates to ~20% higher gross margin.
2. Expand to scenarios combining mobility and simple manipulation (like instant delivery): This represents the next growth phase.
3. Eventually move to more controlled, complex environments (property management, homes): This is the long-term vision, requiring more advanced manipulation capabilities and even richer data.

Broader Implications and Conclusion

The article positions China as a uniquely advantageous market for this “fight to train” strategy due to its scale, urban complexity

一、 背景概述：具身智能的“数据饥渴”与务实出路

当前，以世界模型为核心的具身智能是AI发展的前沿方向，但其发展面临一个根本性挑战：数据。与自动驾驶领域已有海量车辆数据不同，具身智能缺乏普适的“数据采集终端”。酷哇科技认为，没有产品量产就没有真实数据，没有数据模型就无法持续进化。因此，其核心策略不是等待完美的技术，而是**“以战养战”**——让机器人直接投入真实商业运营，在“干活”中收集数据、迭代模型，实现商业与技术的双轮驱动。

二、 核心策略解析：“以战养战”的闭环逻辑

酷哇的“以战养战”是一个清晰的商业与技术正向循环：

1. 数据驱动进化：通过部署万台规模的机器人，在复杂多变的真实城市场景中运营，积累海量、高质的交互数据。这些数据反哺其**通用世界模型（CooWAIM）**的迭代升级。
2. 商业化验证路径：公司选择了技术难度与市场接受度平衡的突破口。首先从环卫清扫、无人出行等开放、刚需场景切入，这些场景对“移动（Drive）”能力要求高，易形成规模。随后，逐步拓展至需要“移动+简单操作（Work）”的即时配送场景，最终目标是进入家庭等半封闭、封闭场景。
3. 经济性闭环：效率直接转化为商业价值。例如，机器人通过强大的实时交互博弈能力提升通行效率20%，就可能直接转化为20%的毛利提升，这是客户愿意付费和规模化的前提。

三、 技术架构解读：统一世界模型与“一脑多形”

为实现跨场景的规模化，酷哇的技术架构是关键：

• 统一的大脑（CooWAIM）：这是一个面向物理世界的通用世界模型，其核心是能够基于环境观测，预测未来并生成行动，将物理因果关系融入决策。这区别于传统的分模块或纯端到端架构。
• 双系统协同：
  • 直觉系统：负责实时感知与避障，确保即时安全。
  • 推理系统：负责理解任务语义与全局规划。
• 一脑多形：同一个AI大脑，通过适配不同的机械形态（如1吨/3吨级环卫车、无人小巴、机器狗），覆盖不同场景。其能力映射为两大核心：
  • Drive（全域移动）：实现从城市主干道到人行道、园区室内的全场景自主移动。
  • Work（多关节操作）：将移动与清扫盘、机械臂等操作深度融合，完成贴边清扫、垃圾分拣等复杂任务。

四、 规模化落地与行业意义

酷哇的实践具有重要的行业示范意义：

1. 已验证的规模化：文章指出，其环卫机器人、无人小巴等已在超过50个城市实现常态化运营并盈利，证明了“在真实场景中赚钱”的商业模式可行。
2. 中国市场的优势：公司强调，中国是全球少有的能支持机器人规模化应用的市场，丰富的城市场景、政策支持和供应链优势，为“以战养战”提供了沃土。
3. 发展路径启示：酷哇的路径清晰展示了具身智能落地的务实阶梯——从“移动”到“操作”，从“开放场景”到“封闭场景”。它告诉行业，不必等到技术完全成熟才部署，而是可以通过分阶段、场景化的商业落地，反向加速技术成熟，最终走向通用智能。

总结而言，酷哇的故事并非单纯的技术炫技，而是一个关于如何将前沿AI技术成功“产品化”和“商业化”的案例。它通过构建数据闭环，解决了具身智能的核心瓶颈，并为中国乃至全球的机器人规模化落地提供了一个可复制的、强调“实战”与“经济性”的样板。