3D / 4D Scene
从真实采集数据重建高保真 3D 场景和时间序列 4D 资产，构建可交互、可扩展的虚拟环境库。Reconstruct high-fidelity 3D scenes and temporal 4D assets from real capture data, building an interactive and extensible virtual environment library.
Lingo World / Simulation & Synthesis Platform
在虚拟世界里训练真实动作Train real actions in virtual worlds
Lingo World 是面向具身智能的仿真与合成数据平台——重建高保真场景、生成物理约束的合成数据、构建长尾和 Corner Case 场景，并在虚拟世界中完成策略测试和 Sim2Real 评估，让机器人在安全部署前获得充分经验。Lingo World is the simulation and synthetic data platform for embodied intelligence — reconstructing high-fidelity scenes, generating physically-constrained synthetic data, building long-tail and corner-case scenarios, and running policy tests with Sim2Real evaluation, so robots gain experience before real deployment.
Core capabilities
四维能力支撑虚拟到真实的桥梁Four capabilities bridging virtual and real
Lingo World 不是传统仿真器。它把 3D/4D 重建、物理仿真、合成数据生成和 Sim2Real 评估统一成一个面向具身智能研发的生产平台。Lingo World is not a traditional simulator. It unifies 3D/4D reconstruction, physics simulation, synthetic data generation, and Sim2Real evaluation into one production platform for embodied intelligence R&D.
Physics Engine
物理仿真、传感器仿真、刚体和软体动力学——在约束条件下生成符合真实物理规律的操作和交互数据。Physics, sensor simulation, rigid-body and soft-body dynamics — generating physically plausible manipulation and interaction data under constraints.
Synthetic Data
合成数据引擎生成长尾场景、Corner Case、罕见事件和多智能体交互，弥补真实采集无法覆盖的数据缺口。Synthetic engine generates long-tail scenarios, corner cases, rare events, and multi-agent interactions, filling gaps that real capture cannot cover.
Sim2Real
策略测试、基准评测、领域随机化和部署前验证——确保在仿真中训练的策略能在真实世界中可靠执行。Policy testing, benchmarks, domain randomization, and pre-deployment validation — ensuring policies trained in simulation execute reliably in the real world.
World generation pipeline
从真实场景到虚拟世界再到合成数据From real scenes to virtual worlds to synthetic data
Lingo World 的生产管线从 Data 平台获取真实数据和元数据起步，经过场景重建、物理绑定、多样本生成和质量验证，最终输出可供 Brain 训练和评估的合成经验。Lingo World's production pipeline starts with real data and metadata from the Data platform, goes through scene reconstruction, physics binding, multi-sample generation, and quality validation, then outputs synthetic experience for Brain training and evaluation.
Reconstruct
从 Lingo Data 获取多模态场景数据，执行 3D 高斯泼溅 / NeRF 重建，生成高保真可导航场景。
Ingest multimodal scene data from Lingo Data; run 3D Gaussian Splatting / NeRF reconstruction to generate high-fidelity navigable scenes.
3D/4D
Bind Physics
绑定材质属性、碰撞体、关节约束、摩擦力、重力和光照模型，让虚拟场景遵循真实物理规律。
Bind material properties, collision bodies, joint constraints, friction, gravity, and lighting — making virtual scenes obey real physics.
Physics
Generate
基于物理约束生成多样操作样本：域随机化、物体摆放变化、光照扰动、长尾和 Corner Case 场景。
Generate diverse manipulation samples under physics constraints: domain randomization, object placement variation, lighting perturbation, long-tail and corner-case scenarios.
Synthesize
Simulate Multi-Agent
多机器人、人机交互和动态障碍场景仿真，在复杂交互环境中测试策略鲁棒性。
Multi-robot, human-robot interaction, and dynamic obstacle simulation — testing policy robustness in complex interactive environments.
Agents
Validate
合成数据质量评估：物理一致性、视觉逼真度、任务覆盖率和 Sim2Real 迁移差距检测。
Synthetic data QA: physics consistency, visual fidelity, task coverage, and Sim2Real transfer gap detection.
QA
Export
标准化输出至 Lingo Brain 训练和评测接口，支持数据版本管理和实验追踪。
Standardized export to Lingo Brain training and evaluation APIs, with version management and experiment tracking.
DeploySim2Real bridge
从仿真策略到真实部署From simulation policy to real deployment
Sim2Real 不是"训练完就部署"的一次性动作。Lingo World 提供领域随机化、增量迁移、差距度量和闭环反馈，让策略从虚拟到真实的过渡可度量、可迭代、可信任。Sim2Real is not a one-shot "train then deploy." Lingo World provides domain randomization, incremental transfer, gap metrics, and closed-loop feedback — making the virtual-to-real transition measurable, iterative, and trustworthy.
领域随机化Domain randomization
在仿真中随机化光照、纹理、物理参数和传感器噪声，训练对视觉和动力学差异鲁棒的策略。Randomize lighting, textures, physics parameters, and sensor noise in simulation to train policies robust to visual and dynamics gaps.
增量迁移与课程学习Incremental transfer & curriculum
从理想仿真逐步引入真实世界约束，按难度递进训练，避免一次性迁移导致的性能崩塌。Gradually introduce real-world constraints from ideal simulation, training with progressive difficulty to avoid catastrophic transfer failure.
Sim2Real 差距度量Sim2Real gap metrics
量化仿真与真实环境的感知、动力学和策略表现差距，为迁移策略调整提供数据依据。Quantify perception, dynamics, and policy performance gaps between simulation and reality, providing data-driven guidance for transfer adjustment.
闭环反馈与持续改进Closed-loop feedback & iteration
真实部署的失败样本和性能数据回流至 World 和 Data，驱动场景库更新和仿真精度提升。Real deployment failures and performance data flow back to World and Data, driving scene library updates and simulation fidelity improvements.
海量定制化数据的持续生成Massive Customized Data, Continuously Generated
从真实场景重建到无限场景泛化，Lingo World 为具身智能提供源源不断、按需定制的高质量仿真数据。From real-scene reconstruction to infinite scenario generalization, Lingo World provides a continuous supply of high-quality, on-demand simulation data for embodied intelligence.
Delivery
平台交付Platform Delivery
仿真平台、场景引擎、评测工具链——以私有化或项目制形态交付，深度对接 Brain 训练与持续迭代。Simulation platform, scene engine, evaluation toolchain — delivered privately or as project services, deeply integrated with Brain training and iteration.
Simulation Platform
私有化部署的仿真平台，包含场景编辑器、任务构建器、批量仿真引擎和结果分析面板。Private simulation platform with scene editor, task builder, batch simulation engine, and results dashboard.
Scene Library
预置实验室、工厂、家庭、园区等场景库，支持从 Data 导入真实场景进行重建和扩展。Pre-built scene library covering labs, factories, homes, and parks — supporting real-scene import from Data for reconstruction and expansion.
Synthesis Engine
合成数据生成引擎：参数化场景生成、长尾场景构建、多智能体编排和批量数据导出。Synthetic data generation engine: parametric scene generation, long-tail construction, multi-agent orchestration, and batch data export.
Benchmarks & APIs
标准评测基准、策略评估 API 和 Sim2Real 评估工具链，对接 Brain 训练和 CI/CD 流程。Standard benchmarks, policy evaluation APIs, and Sim2Real evaluation toolchain, integrated with Brain training and CI/CD pipelines.
Where it applies
从科研仿真到工业数字孪生From research simulation to industrial digital twins
Lingo World 不只是机器人仿真，而是面向任何需要在虚拟世界中生成经验、测试行为和验证风险的具身智能系统。Lingo World is not just robot simulation — it serves any embodied intelligence system that needs to generate experience, test behavior, and validate risk in virtual worlds.
科学实验仿真Scientific experiment simulation
虚拟实验室场景、化学反应和物理操作仿真，为 AI Lab 提供策略预训练和安全验证环境。Virtual lab scenes, chemical reaction and physical manipulation simulation — providing policy pretraining and safety validation for AI Lab.
自动驾驶仿真Autonomous driving simulation
生成 Corner Case、罕见天气和复杂交通场景，弥补真实路采无法覆盖的长尾数据缺口。Generate corner cases, rare weather, and complex traffic scenarios, filling long-tail gaps that real-world driving data cannot cover.
工业数字孪生Industrial digital twins
产线、仓储和物流场景的数字孪生，在虚拟环境中验证机器人策略、优化布局和评估风险。Digital twins of production lines, warehouses, and logistics — validating robot policies, optimizing layouts, and assessing risk in virtual environments.
多智能体协作Multi-agent collaboration
人机协作、多机器人任务分配和动态环境交互仿真，测试复杂系统中的协调与鲁棒性。Human-robot collaboration, multi-robot task allocation, and dynamic environment interaction simulation — testing coordination and robustness in complex systems.