Built Over 100 Data Training Sites, Why Are Robots Still 'Hungry'?
建了百余座数据训练场，机器人为何还在“饿肚子”
The article explores the issue of data scarcity in the field of embodied intelligence, pointing out that although there are already a large number of data collection centers, high-quality and reusable data remains severely insufficient. The article analyzes structural contradictions such as high data collection costs, inconsistent quality, fragmented formats, and lack of cross-ontology reusability. It introduces Wu Wen Tech's solution of building a data foundation through a Real2Sim2Real closed-loop system, including large-scale data collection, automated annotation, and simulation training technologies, ultimately forming a data-driven flywheel to promote the development of embodied intelligence.
Full article
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The article highlights the critical data scarcity in embodied AI, emphasizing the gap between the scale of robot models and the availability of high-quality physical interaction data. It identifies structural issues such as high collection costs, quality inconsistencies, and lack of standardization as major barriers. The solution proposed by Wu Wen Tech involves a Real2Sim2Real closed-loop system,
What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
Reported duration
500,000 hours
- dataset: not_reported
What remains unknown
Not established in the collected evidence: Environment, Control, Data origin.
Reported performance applies to the described task. It does not establish general autonomy or deployment readiness.
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
国内真实物理交互场景合规数据仅50万小时，而机器人商业化落地需要数千万小时数据，缺口超过99%
Open source E1
具身基础模型要迎来“ChatGPT时刻”至少需要数千万小时数据，当前全球可用的高质量真实数据仅为数十万至百万小时级
Open source E2
模型在篮子收纳任务中的成功率提升约20%
Open source E3
模型仿真成功率由9.7%提升至79.8%
Open source E4
Implications for data suppliers
RoboSignal interpretation and collection questions, not statements of buyer demand.
- Confirm the required data type and collection setting with the buyer; this source does not establish a complete collection specification.
- Compare the reported units and scope before using these quantities in a budget. Recording hours, sensor-hours and trajectories are different measures.
- Validate demand and acceptance criteria with a buyer before scaling. Publication, popularity and a research result do not establish a purchase commitment.
Source：RoboSpeak — WeChat · mp.weixin.qq.com