2026 Embodied Large Model Layered Map: From Model Release to Production Line
Overview
This article observes the technical paths and scene deployment progress of five Chinese companies in the field of world models and embodied intelligence. It evaluates their core competencies and development prospects, emphasizing the transition from parameter scale to system-level performance and the importance of real-world data feedback in model iteration.
From Chinese robotics — Robot learning research
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · collision response time: 20 other · Basis not reported
“核心性能：碰撞后 20 毫秒安全反射（传统系统 > 200 毫秒），急动度峰值降低 80.2%，“脊髓”层功耗仅 0.4 瓦。”
Exact source · revision 2Source owner not reported
Reported quantity · peak jerk reduction: 80.2 other · Basis not reported
“核心性能：碰撞后 20 毫秒安全反射（传统系统 > 200 毫秒），急动度峰值降低 80.2%，“脊髓”层功耗仅 0.4 瓦。”
Exact source · revision 2Source owner not reported
Reported quantity · spinal layer power consumption: 0.4 other · Basis not reported
“核心性能：碰撞后 20 毫秒安全反射（传统系统 > 200 毫秒），急动度峰值降低 80.2%，“脊髓”层功耗仅 0.4 瓦。”
Exact source · revision 2Source owner not reported
Reported quantity · core component runtime: 20000 hours · Basis not reported
“自有产线年产能 2000 台，月出货超百台，核心部件无故障运行 2 万-5 万小时，2026 下半年启动国内首个数万台产线建设。”
Exact source · revision 2Source owner not reported
Reported quantity · Reported success rate: 98.6 percent · Basis not reported
“公开评测中，ω-EVA在LIBERO达到98.6%成功率，LIBERO-PLUS达到83.4%，RoboTwin达到90.3%。”
Exact source · revision 2Source owner not reported
Reported quantity · parameters in JEPA-WAM: 0.5 other · Basis not reported
“JEPA-WAM采用Shared Predictor共享预测器，学习跨任务环境时序变化规律，基座为0.5B参数，无需大规模机器人预训练数据和像素级未来视频生成，相关代码已开源。”
Exact source · revision 2Source owner not reported
Reported quantity · calculation power of T5 and N5: 2070 other · Basis not reported
“T5与N5搭载Jetson Thor T5000，提供2070 TFLOPS算力，已在智元机器人精灵G2上批量出货。”
Exact source · revision 2Source owner not reported
Artifact availability · code: reported pending
“JEPA-WAM采用Shared Predictor共享预测器，学习跨任务环境时序变化规律，基座为0.5B参数，无需大规模机器人预训练数据和像素级未来视频生成，相关代码已开源。”
Exact source · revision 2Source owner not reported
Evidence dependency changes (6)
- 2026-10-30T22:31:59.446Z · evidence updated · source revision 2. Evidence extraction was updated; current source attributions are shown above.
- 2026-10-30T22:31:42.663Z · evidence updated · source revision 2. Evidence extraction was updated; current source attributions are shown above.
- 2026-10-30T20:55:35.056Z · evidence updated · source revision 2. Evidence extraction was updated; current source attributions are shown above.
- 2026-10-30T19:15:22.163Z · evidence updated · source revision 2. Evidence extraction was updated; current source attributions are shown above.
- 2026-10-30T19:15:03.227Z · evidence updated · source revision 2. Evidence extraction was updated; current source attributions are shown above.
- 2026-10-30T17:19:01.813Z · evidence updated · source revision 2. Evidence extraction was updated; current source attributions are shown above.
Developments
- 2026-10-28 20:50 UTC · 1 reports2026 Embodied Large Model Layered Map: From Model Release to Production LineChinese robotics — Robot learning research：From Model Release to Production Line Validation: 2026 Embodied Large Model 'Real Power' Layered Map
- 2026-10-18 15:46 UTC · 1 reportsIndustry analysis of world models and embodied AI technical paths for five 2026Chinese robotics — Robot learning research：World Models × Embodied Intelligence | 2026 Five Representative Companies' Technical Paths and Scene Deployment Observations
Report timeline
Follow attributed reports and material updates.
- Chinese robotics — Robot learning researchFrom Model Release to Production Line Validation: 2026 Embodied Large Model 'Real Power' Layered Map
In 2026, embodied intelligence has moved beyond concept validation into scaled commercial deployment. The article evaluates the progress of domestic companies in deploying embodied large models, categorizing them into four layers based on their ability to pass through complete iteration paths, achieve multi-scenario replication, and comply with international regulations. It highlights AI²Robotics (智平方) as a leading example, showcasing its four-generation model development, cross-industry applications, and compliance in international markets.
- Chinese robotics — Robot learning researchSignalWorld Models × Embodied Intelligence | 2026 Five Representative Companies' Technical Paths and Scene Deployment Observations
This article observes the technical paths and scene deployment progress of five Chinese companies in the field of world models and embodied intelligence. It evaluates their core competencies and development prospects, emphasizing the transition from parameter scale to system-level performance and the importance of real-world data feedback in model iteration.
Event coverage history
There is not enough continuous observation data to show a trend.
Timezone · UTC
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