Cross-Embodiment 平台
CROSS-EMBODIMENT PLATFORM · 跨本体平台

让数据跨越机型边界持续流动复用 Unlocking industrial datafrom single-embodiment lock-in

不同机器人本体、传感器布局、控制接口和任务空间并不兼容。Cross-Embodiment 平台通过六层架构，把不同设备、不同工位、不同本体采来的数据，映射为可复用、可评测、可迁移的统一表示。 Different robot embodiments, sensor layouts, control interfaces and task spaces are incompatible. The Cross-Embodiment platform uses a six-layer architecture to map data from different devices, workstations and embodiments into unified representations that are reusable, measurable and transferable.

为什么需要跨本体Why Cross-Embodiment 六层架构6-Layer Architecture
跨本体映射Cross-Embodiment Mapping 统一任务语义Unified Task Semantics 本体 AdapterEmbodiment Adapter 原子技能库Atomic Skill Library
为什么需要 Cross-EmbodimentWhy Cross-Embodiment

工业数据的本体锁定困境The embodiment lock-in dilemma

普通数采工厂只关注"让机器人模仿动作"，数据被限制在特定机型、工位、工况中。换一款机器人、换一条产线、换一种材料，往往需要重新采集与适配。清研精准通过 Cross-Embodiment 平台，让数据跨越机型边界，持续流动复用。 Ordinary data capture factories focus only on "making robots mimic motions" — data gets locked to a single embodiment, workstation and condition. Changing robots, production lines or materials often requires re-collection and re-adaptation. Tsing Standard's Cross-Embodiment platform frees data from embodiment lock-in.

PROBLEM 01

本体差异大Embodiment variance

机械臂、灵巧手、人形机器人、AMR、无人机、工程机械的动作空间、传感器布局和控制接口完全不同，数据格式互不兼容。Robotic arms, dexterous hands, humanoids, AMRs, drones and machinery have completely different action spaces, sensor layouts and control interfaces with incompatible data formats.

PROBLEM 02

任务语义不统一Task semantics mismatch

同样是"抓取"，在不同工位、不同物体、不同夹具下，状态表示、动作序列和结果判定标准都不一样，无法复用。The same "grasp" task has different state representations, action sequences and success criteria across workstations, objects and fixtures — impossible to reuse directly.

PROBLEM 03

数据难以复用Data hard to reuse

在机械臂上采集的插接数据，无法直接用于人形机器人；在一条产线上有效的技能，换产线后性能急剧下降，数据价值大量损耗。Insertion data captured on a robotic arm can't be directly used for humanoids; skills effective on one line degrade sharply on another, wasting data value.

跨本体技能迁移Cross-Embodiment Skill Transfer

同一技能数据，适配不同机型，无需从零采集Same skill data adapts to different robot platforms without re-collection

原子技能库Atomic Skill Library

持续沉淀可组合的基础技能单元，覆盖工业主流操作场景Continuously accumulating composable skill units covering mainstream industrial operations

迁移成功率显著提升Significant Transfer Success Rate Improvement

经过 Cross-Embodiment 映射后，新本体在目标任务上的成功率大幅优于直接迁移基线After Cross-Embodiment mapping, new embodiments achieve significantly higher success rates than direct transfer baselines

六层平台架构Six-Layer Platform Architecture

从多源异构数据到可执行原子技能From heterogeneous multi-source data to executable atomic skills

Cross-Embodiment 平台通过六层架构，将来自不同本体、不同工位、不同传感器的原始数据，逐层抽象为可跨本体执行和评测的统一技能表示。 The Cross-Embodiment platform uses a six-layer architecture to progressively abstract raw data from different embodiments, workstations and sensors into unified skill representations executable and evaluable across embodiments.

LAYER 01 输入层Input Layer

多源异构数据输入Heterogeneous Multi-Source Data Input

接入来自不同本体、不同工位、不同传感器的原始数据流，统一接入协议与数据格式。Ingesting raw data streams from different embodiments, workstations and sensors with unified access protocols and data formats.

机械臂 Arm灵巧手 Hand人形 HumanoidAMR无人机 UAV工程机械 MachineryPLC-MES
LAYER 02 语义层Semantic Layer

任务语义对齐Task Semantic Alignment

将插接、抓取、拧紧、检测、巡检等任务拆解为标准步骤和技能单元。不同本体执行同一任务时，共享任务目标、前置条件、后置条件和异常处理逻辑。Decomposing tasks like insertion, grasping, tightening, inspection and patrol into standard steps and skill units. Different embodiments executing the same task share task goals, preconditions, postconditions and exception handling logic.

任务目标提取步骤拆解前后置条件异常处理逻辑
LAYER 03 状态层State Layer

统一物理状态表示Unified Physical State Representation

对齐物体、环境、设备状态、接触关系和执行结果。把不同传感器采集的原始信号（视觉、力觉、触觉、惯性、位置）映射到统一的物理状态空间。Aligning objects, environment, device status, contact relationships and execution results. Mapping raw signals from different sensors (vision, force, tactile, inertial, position) to a unified physical state space.

物体状态环境状态接触关系执行结果异常标记
LAYER 04 适配层Adapter Layer

本体 Adapter 映射Embodiment Adapter Mapping

将同一技能适配到机械臂、人形、AMR、无人机、工程机械等不同动作空间。通过运动学逆解、动力学补偿和控制接口转换，让技能跨本体执行。Adapting the same skill to different action spaces like robotic arms, humanoids, AMRs, drones and machinery. Enabling cross-embodiment skill execution through inverse kinematics, dynamics compensation and control interface conversion.

运动学逆解动力学补偿控制接口转换传感器差异对齐
LAYER 05 技能层Skill Layer

原子技能库Atomic Skill Library

沉淀可订阅、可授权、可复用的原子技能库。每条技能包含：任务语义、物理状态表示、本体适配参数、成功率评测结果和异常处理策略。Accumulating subscribable, licensable, reusable atomic skill library. Each skill includes: task semantics, physical state representation, embodiment adaptation parameters, success rate evaluation and exception handling strategies.

抓取 Grasp插接 Insert拧紧 Tighten检测 Inspect搬运 Transport巡检 Patrol组装 Assemble焊接 Weld
LAYER 06 评测层Evaluation Layer

跨本体评测与验证Cross-Embodiment Evaluation & Validation

判断技能是否能跨工况、跨设备、跨产线复用。评测指标包括成功率、稳定性、泛化能力、异常处理和边界 case 覆盖度，结合仿真验证与真机测试。Determining whether skills can be reused across conditions, devices and production lines. Metrics include success rate, stability, generalization, exception handling and edge case coverage, combining simulation and real-machine testing.

成功率稳定性泛化能力异常处理仿真验证真机测试
Cross-Embodiment 数据看板
CROSS-EMBODIMENT · DATA DASHBOARD
具身智能多视角数据采集
EMBODIED DATA · MULTI-VIEW CAPTURE
四大核心能力Four Core Capabilities

从本体锁定到跨本体复用From embodiment lock-in to cross-embodiment reuse

CAPABILITY 01

统一任务语义Unified Task Semantics

将插接、抓取、拧紧、检测、巡检等任务拆解为标准步骤和技能单元。不同本体执行同一任务时，共享任务目标、前置条件、后置条件和异常处理逻辑。Decomposing tasks like insertion, grasping, tightening, inspection and patrol into standard steps and skill units. Different embodiments executing the same task share task goals, preconditions, postconditions and exception handling logic.

CAPABILITY 02

统一物理状态表示Unified Physical State Representation

对齐物体、环境、设备状态、接触关系和执行结果。把不同传感器采集的原始信号（视觉、力觉、触觉、惯性、位置）映射到统一的物理状态空间。Aligning objects, environment, device status, contact relationships and execution results. Mapping raw signals from different sensors (vision, force, tactile, inertial, position) to a unified physical state space.

CAPABILITY 03

本体 Adapter 映射Embodiment Adapter Mapping

将同一技能适配到机械臂、人形、AMR、无人机、工程机械等不同动作空间。通过运动学逆解、动力学补偿和控制接口转换，让技能跨本体执行。Adapting the same skill to different action spaces. Enabling cross-embodiment skill execution through inverse kinematics, dynamics compensation and control interface conversion.

CAPABILITY 04

跨本体评测Cross-Embodiment Evaluation

判断技能是否能跨工况、跨设备、跨产线复用。评测指标包括成功率、稳定性、泛化能力、异常处理和边界 case 覆盖度。Determining whether skills can be reused across conditions, devices and production lines. Evaluation metrics include success rate, stability, generalization capability, exception handling and edge case coverage.

与普通数采工厂对比Comparison with Data Capture Factories

清研精准不是数采工厂，而是跨本体平台Tsing Standard is not a data factory, but a cross-embodiment platform

普通数采工厂只关注"让机器人模仿动作"，清研精准关注"让模型理解工业物理过程"。 Ordinary data capture factories focus on "making robots mimic motions" — Tsing Standard focuses on "making models understand industrial physical processes."

普通数采工厂Data Capture Factory

  • 起点Start单一机器人本体Single robot embodiment
  • 数据对象Data动作轨迹、遥操作样本Motion trajectories, teleoperation samples
  • 目标Goal让机器人模仿动作Make robots mimic motions
  • 复用Reuse依赖同类机器人Depends on same robot type
  • 验证Validation离线训练或 demoOffline training or demo
  • 泛化Generalization换工况、换本体后性能下降Degrades with condition/embodiment change

清研精准 Cross-EmbodimentTsing Standard Cross-Embodiment

  • 起点Start真实工业现场Real industrial sites
  • 数据对象Data状态—动作—结果—异常闭环State-action-result-anomaly loop
  • 目标Goal让模型理解工业物理过程Make models understand industrial physics
  • 复用ReuseCross-Embodiment 跨本体映射Cross-embodiment mapping
  • 验证Validation仿真评测 + 测试验证 + 现场反馈Simulation + testing + on-site feedback
  • 泛化Generalization跨工况、跨本体、跨产线复用Reusable across conditions/embodiments/lines
应用场景Use Cases

Cross-Embodiment 在真实工业场景中的应用Cross-Embodiment in real industrial scenarios

从整车总装到矿山巡检，Cross-Embodiment 平台让技能跨本体、跨工况、跨产线复用。 From vehicle assembly to mine inspection, the Cross-Embodiment platform enables skill reuse across embodiments, conditions and production lines.

CASE 01

整车总装插接技能Vehicle Assembly Insertion

在机械臂上采集的插接技能，通过 Cross-Embodiment 平台映射到人形机器人，成功率显著提升，技能开发周期大幅缩短。Insertion skills captured on robotic arms, mapped to humanoids via Cross-Embodiment platform, with significantly improved success rates and greatly reduced development cycles.

CASE 02

矿山巡检跨本体复用Mine Inspection Cross-Embodiment

在地面 AMR 上训练的巡检技能，通过本体 Adapter 映射到井下四足机器人和无人机，覆盖复杂地形，巡检覆盖率提升 3×。Inspection skills trained on surface AMRs, mapped to underground quadrupeds and drones via embodiment adapters, covering complex terrain with 3× inspection coverage improvement.

CASE 03

电网巡检多本体协同Grid Inspection Multi-Embodiment

地面巡检车、爬杆机器人、无人机三种本体共享统一任务语义和物理状态表示，协同完成巡检任务，异常发现率提升 40%。Ground patrol vehicles, pole-climbing robots and drones share unified task semantics and physical state representation, collaborating on inspection with 40% improvement in anomaly detection.

深入了解技术平台与产品Dive deeper into platform & products

探索工业认知系统、完整技术平台和产品矩阵。Explore industrial cognition system, complete platform and product matrix.

工业认知系统World Model → 技术平台Platform 产品与服务Products