理解人Understand people
融合语言指令、场景观测、任务进度和历史记忆，理解复杂任务意图、执行约束和人类纠偏。Fuses language, scene observation, task progress, and memory to understand intent, constraints, and human correction.
Lingo Brain / World Action Model
面向真实世界机器人的具身大脑The embodied brain for real-world robots
Lingo Brain 将 World Action Model 产品化，嵌入机器人、智能设备和边缘系统，让机器理解人、会操作、能实时闭环，并在真实反馈中持续进化。Lingo Brain productizes the World Action Model inside robots, devices, and edge systems, enabling machines to understand people, operate physically, close the loop in real time, and improve through feedback.
Three-in-one capability
理解人、会操作、实时闭环Understand people. Operate physically. Close the loop.
面向真实场景的机器人，需要同时理解人类意图、生成可执行动作，并在部署反馈中持续学习。Lingo Brain 将这三类能力统一到同一个具身智能引擎中。Robots in real environments need to understand human intent, generate executable actions, and keep learning from deployment feedback. Lingo Brain unifies these capabilities in one embodied intelligence engine.
会操作Operate physically
基于物理环境、物体关系和硬件约束，规划长程任务，生成可执行、可适配、可评估的动作。Plans long-horizon tasks and generates executable, adaptable, and evaluable actions from physical context and hardware constraints.
实时闭环Close the loop
连接感知、推理和控制链路，将失败样本、执行结果和偏好反馈转化为持续学习的数据。Connects perception, inference, and control while turning failures, execution results, and preference feedback into learning data.
Inside the engine
基于 WAM 的动作生成与状态预测Action generation and state prediction with WAM
Lingo Brain 不只输出下一步动作。它把当前状态、任务目标、人类输入和先验约束编码进同一个交互式模型，在生成动作的同时预测状态演化，形成“感知-决策-预测”的闭环。Lingo Brain does not only output the next action. It encodes state, goals, human input, and prior constraints into an interactive model, generating actions while predicting state evolution.
多源上下文统一编码Unified context encoding
任务目标、环境状态、子任务、历史记忆与元数据进入同一决策上下文。Goals, state, subtasks, memory, and metadata share one decision context.
动作-状态联合建模Action-state joint modeling
兼顾操作精度与长程稳定性，输出动作并预测下一状态。Balances manipulation precision and long-horizon stability by predicting the next state.
安全约束与人类引导Safety constraints and human guidance
融合规则、偏好反馈、动作过滤和执行结果，持续更新经验池。Combines rules, preference feedback, action filters, and results to update the experience pool.
Deployment loop
Brain 是闭环的智能出口Brain is the intelligence outlet of the loop
Lingo Brain 承接 Trace、Data、World 生产出的真实数据、训练资产和仿真经验，再把真实部署反馈回流，驱动下一轮数据采集、训练和验证。Lingo Brain consumes real data, training assets, and simulated experience from Trace, Data, and World, then returns deployment feedback to drive the next cycle.
Delivery
为机器人和物理系统集成而交付Delivered for robot and physical-system integration
Lingo Brain 不是独立登录使用的 SaaS 控制台，而是面向本体、设备、自动化系统和边缘节点的模型与运行时能力。Lingo Brain is not a standalone SaaS console. It is a model and runtime capability for embodiments, devices, automation systems, and edge nodes.
Model Package嵌入式模型包 / 私有化部署包Embedded model package / private deployment
Edge Runtime边缘推理 Runtime，与 Dora 底座协同Edge inference runtime, coordinated with the Dora foundation
SDK/API面向机器人、实验设备和自动化系统的集成接口Integration interfaces for robots, lab devices, and automation systems
Skill Pack面向场景任务的技能包、模型更新和持续学习服务Scenario skill packs, model updates, and continuous learning services
Where it lands
从科研机器人走向更多真实本体From scientific robots to more real embodiments
Lingo Brain 面向需要物理操作、空间理解和人机协作的真实系统，可嵌入科研机器人、智能设备、自动化作业系统，并逐步扩展到更多服务型本体。Lingo Brain serves real systems that require physical operation, spatial understanding, and human-machine collaboration, from scientific robots and intelligent devices to automation systems and future service embodiments.
科学实验机器人Scientific robots
自主移动、长程任务规划、搬运上下料、仪器操作、状态确认。Autonomous movement, long-horizon planning, sample transfer, instrument operation, state confirmation.
智能设备系统Intelligent devices
把设备操作、界面交互和边缘感知纳入统一任务上下文。Unifies device operation, interface interaction, and edge perception in one task context.
自动化作业系统Automation systems
在安全约束、任务约束和硬件约束下生成可执行策略。Generates executable policies under safety, task, and hardware constraints.
未来服务本体Future service embodiments
从人机协作世界动作模型出发，扩展到家庭、教育和服务场景。Extends from human-robot world-action models into home, education, and service contexts.