Meituan Battlefield Investment leads the investment, and the embodied intelligence company has completed hundreds of millions of yuan in Series A financing, with a cumulative financing of over 1 billi

Yesterday's news revealed that the independent variable robot of embodied intelligence company has recently completed hundreds of millions of yuan in Series A financing, led by Meituan Zhantou and followed by Meituan Longzhu.

It is reported that this round of financing will be used to continuously accelerate the synchronous iteration of fully self-developed end-to-end universal embodied intelligent large models and robot bodies, as well as the cooperation and implementation of intelligent solutions for multiple application scenarios in the future.

In addition to the hundreds of millions of yuan Pre-A++round financing led by Lightspeed Photosynthesis and Junlian Capital, which was disclosed to the public in February this year, the independent variable robot also completed hundreds of millions of yuan Pre-A++round financing invested by Huaying Capital, Yunqi Capital, and Guangfa Xinde before the A-round financing. In less than a year and a half since its establishment, the independent variable robot has completed 7 rounds of financing, with a cumulative financing amount exceeding 1 billion yuan.

The independent variable robot was established in December 2023, dedicated to achieving the ultimate goal of universal robots through the development of embodied intelligent general models - universal robots can autonomously perform tasks through interaction, perception, and action like humans, with efficient generalization and transfer capabilities.

The company focuses on the research and development of "universal embodied large models" and is the earliest company in China to achieve end-to-end unified embodied large models. Currently, the WALL-A of the Great Wall operation large model series, independently developed by the independent variable, has reached world-class leading levels in multiple performance aspects, enabling robots to autonomously perceive, judge, and operate to complete complex and precise physical world tasks.

In terms of team, the founder and CEO of the company, Wang Qian, graduated from Tsinghua University with a bachelor's and master's degree. He is one of the earliest researchers in the world to propose attention mechanisms in neural networks. During his doctoral studies, he participated in multiple Robotics Learning research projects in top robotics laboratories in the United States, covering almost all fields related to robot operation and home service robots.

Co founder and CTO Wang Hao holds a PhD in Computational Physics from Peking University. He previously served as the Algorithm Leader of the Fengshen List Big Model Team at the IDEA Research Institute in the Greater Bay Area of Guangdong, Hong Kong, and Macau. He led the development of Ziya, the first billion level big model in China and one of the earliest batch of billion level big models.

At the third forum on the development of embodied intelligent robot industry recently, Wang Hao, co-founder and CTO of independent variable robot, talked about that traditional industrial automation and robot technology have many bottlenecks, such as pre programming and fixed trajectory technology, which cannot solve the complex interaction problems in the real physical world.

Wang Hao pointed out that the development of large models has brought opportunities to break through the ceiling of traditional robotics, enabling it to handle unstructured scenes and diverse tasks, replacing multiple small models with a universal large model, and reducing the need for early modeling. Although the hardware performance of robots surpasses that of human hands, they still fall short in autonomous operation of complex tasks, with issues such as system instability and sensor failure limiting their performance. The randomness and complexity of the physical world cannot be fully perceived, planned in advance, or even fully described in language. Humans learn complex tasks through personal experience and interaction, while robots rely solely on language descriptions to master similar skills. The future direction is to enable robots to learn like humans, forming experiences through self-evaluation, reflection, and behavior adjustment, and promoting better learning.

The practice and exploration of independent variable robots in general embodied intelligence models - enabling robots to learn like humans - is the key leap for robots to achieve general intelligence.