Chengdu Ruixinhang Technology Completes New Financing Round with Innovation Workshop Among Investors
Overview
Chengdu Ruixinhang Technology Co., Ltd., founded in 2019 by professors Zhou Jun and Xu Ling, recently completed a new financing round worth tens of millions yuan led by Innovation Workshop, Zhongli Shares, and Today International. The company focuses on embodied AI for industrial scenarios, having developed mobile robot controllers and now extending capabilities to full robots. It claims its brain-inspired model enables zero-training picking for new parts, reducing deployment time and hardware costs, though specific performance metrics remain unverified independently.
Generated from attributed reports · Updated 3 days ago
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 · article length: 3000 other · Basis not reported
“作者 | 乔钰杰 编辑 | 袁斯来 本文约3000字，建议阅读8分钟 硬氪获悉，成都睿芯行科技有限公司（以下简称“睿芯行”）近日完成数千万元新一轮融资，投资方包括创新工场、中力股份及今天国际。”
Exact source · revision 2Source owner not reported
Reported quantity · company founding year: 2019 other · Basis not reported
“睿芯行成立于2019年，由长江学者特聘教授、电子科技大学教授周军与西南交通大学教授徐菱联合创立，聚焦工业场景下的 具身智能 技术与产品研发。”
Exact source · revision 2Source owner not reported
Reported quantity · Reported duration: 8 other · Basis not reported
“作者 | 乔钰杰 编辑 | 袁斯来 本文约3000字，建议阅读8分钟 硬氪获悉，成都睿芯行科技有限公司（以下简称“睿芯行”）近日完成数千万元新一轮融资，投资方包括创新工场、中力股份及今天国际。”
Exact source · revision 2Source owner not reported
Report timeline
Follow attributed reports and material updates.
- Chinese robotics — Robot learning researchSignalChengdu Ruixinhang Technology Completes New Financing Round with Innovation Workshop Among Investors
Chengdu Ruixinhang Technology Co., Ltd., founded in 2019 by professors Zhou Jun and Xu Ling, recently completed a new financing round worth tens of millions yuan led by Innovation Workshop, Zhongli Shares, and Today International. The company focuses on embodied AI for industrial scenarios, having developed mobile robot controllers and now extending capabilities to full robots. It claims its brain-inspired model enables zero-training picking for new parts, reducing deployment time and hardware costs, though specific performance metrics remain unverified independently.
Timezone · UTC
Article dates follow your selected timezone. Briefing editions use Hong Kong time (UTC+8).