Hard Krypton Exclusive: Alumni from Tsinghua University's School of Vehicle and Mobility Launch Embodied AI Startup, Secures Hundreds of Millions in Angel Financing for Automotive Industry Deployment

Author | Qiu Xiaofen

Editor | Yuan Silai

Yingke learned that the embodied intelligence company 「Guangxiang Technology」 announced the completion of an angel - round financing totaling hundreds of millions of yuan.

The latest round was deeply participated in by leading financial and industrial investors such as Zhuhai Science and Technology Industry Group, Xingzheng Capital, Songhe Capital, Shunxi Fund, Muhua Kechuang, SeeFund, Yichen Capital, and the listed company Xingyun Technology. Old shareholders, Zero One Ventures and L2F Light Source Entrepreneurs Fund, continued to increase their investments.

The funds from this round will be mainly invested in the R & D and iteration of the physical - native base model, and will promote the commercial delivery of embodied intelligent robots in industrial scenarios.

「Guangxiang Technology」 was established in April 2025. It is an embodied intelligence company jointly incubated by the School of Vehicle and Mobility and the School of Artificial Intelligence of Tsinghua University.

In terms of team configuration, 「Guangxiang Technology」 shows a "industry + academia" gene. Zhang Tao, the founder and CEO of the company, once served as the person - in - charge of Gaode's spatial perception engine. The technology he led has been mass - produced and applied to millions of vehicle terminals.

Professor Li Shengbo, the co - founder, is an internationally well - known expert in the fields of reinforcement learning and autonomous driving. He has published more than 250 papers, with over 30,000 citations, and has been selected as a highly cited Chinese scholar by Elsevier for five consecutive years.

Other members of the company's team mainly come from technology and robotics companies such as Alibaba, Tencent, Huawei, KUKA, and Geek+ and have rich experience in full - stack robot technology and commercial implementation.

In terms of the choice of technical route, 「Guangxiang Technology」 has chosen a path different from the mainstream VLA (Vision - Language - Action) and video - prediction - based world models.

Zhang Tao told Yingke that the VLA route is essentially a mapper between perception and action, relying on the imitation of human demonstration data and having limited generalization ability. The video - prediction - based world model only focuses on pixel - level prediction and cannot depict physical properties such as mass, inertia, and friction, making it difficult to support the generation of general and generalized actions.

In his view, true physical - native intelligence is the ability to emerge autonomously in the process of perception, interaction, feedback, exploration, and constraint with the physical world. Therefore, the physical - native base model must take physical interaction as the primary principle and be able to continuously learn the laws of the world, behavioral consequences, and task constraints from the physical environment.

For this reason, 「Guangxiang Technology」 has proposed a technical route of the "physical - native base model". Its core logic is to enable the model to autonomously understand physical laws in the interaction with the physical environment, rather than simply imitating human actions.

To support this route, the company has built a "trinity" technical system composed of the reinforcement learning algorithm matrix Phi - RL Matrix, the physical data asset Phi - Space, and the general physical intelligence development platform Phi - Arch in a coordinated manner —

At the algorithm level, the company has self - developed the top - notch reinforcement learning algorithm matrix Phi - RL Matrix in the industry. Different from the traditional "learning actions by watching videos", the core logic of this algorithm is to enable robots to independently master the operating laws of the physical world through massive trial - and - error in both simulated and real environments.

At the data level, relying on the physical data asset Phi - Space, 「Guangxiang Technology」 has reconstructed a large number of real scenarios in the virtual world. Using self - developed 3D and physical modeling technologies, the system can not only reproduce the geometric shapes of real scenarios but also accurately simulate physical properties such as mass, friction, and deformation. Combined with generative models, the simulated scenarios can expand exponentially, providing almost infinite and low - cost data fuel for algorithm training.

At the platform level, the general physical intelligence development platform Phi - Arch of 「Guangxiang Technology」 has solved the problem of technology reuse. Specifically, it has built a stable engineering system for simulation, training, verification, and deployment, and has precipitated the underlying data, algorithms, tools, etc. of model training into standardized assets to ensure the stability of continuous model iteration during the rapid technological update process and enable the "learning results" of robots to be quickly migrated and implemented.

After laying a solid technical foundation, 「Guangxiang Technology」 has also quickly launched its products.

In June 2026, 「Guangxiang Technology」 officially released the industrial - grade self - evolving embodied intelligent robot Phi - Bot X1. This robot, designed specifically for industrial scenarios, uses a four - steering - wheel omnidirectional chassis, supporting active steering, lateral "crab walking", diagonal movement, and in - place rotation. It can lock itself autonomously during operation to ensure stability. Its lifting waist structure enables its vertical working range to cover from 0 to 2.5 meters.

According to Zhang Tao, Phi - Bot X1 has 27 degrees of freedom throughout its body. The dual - arm with full - joint force control supports 1kHz collaborative control and real - time force feedback. It can achieve a positioning accuracy of 10mm and a repeat positioning accuracy of 0.05mm at the end only relying on its own perception, and supports quick battery replacement in 1 minute.

Different from the common practice in the industry of "releasing PPT first and then doing product and scenario verification", before the official release of the product, the robots of 「Guangxiang Technology」 had already completed real - world operation verification on the automobile production line.

(Image source/Enterprise) 

At the 2026 ATC exhibition, Phi - Bot X1 ran continuously for 21.5 hours over three days, completing the entire process of welding, loading, and unloading on the automobile production line with zero errors and zero interruptions.

In the operation of aligning two holes simultaneously, it controlled the dynamic operation accuracy within the millimeter - level and the angle accuracy within 0.3° only relying on its own perception, with a 100% success rate of continuous operation in a dynamic environment.

Relying on the generalized skill model, in the mobile quality inspection scenario, Phi - Bot X1 increased the efficiency by 51% compared with the non - collaborative method, saved 25% to 45% of the cycle time compared with manual workstations, and compressed the deployment cycle from more than six months in traditional automation to weeks or even days.

(Image source/Enterprise) 

After initially verifying the feasibility of commercial implementation, 「Guangxiang Technology」 will rapidly expand the scale of implementation in the future.

Zhang Tao told Yingke that in the century - long automation process of the automobile industry, all the problems that can be solved by existing technical means have already been solved. Embodied robots should tackle the 30% digital gap that "robotic arms and PLCs cannot solve", such as welding, loading, and unloading with the risk of burns, tail - line quality inspection that requires flexible adjustment of the perspective and high consistency, and high/medium/low - position assembly that is unfriendly to workers' cervical and lumbar spines.

Currently, 「Guangxiang Technology」 has completed real - world scenario verification around typical high - value workstations such as loading, unloading, and quality inspection in automobile manufacturing and has reached commercial cooperation with several domestic and international leading automobile enterprises.

Zhang Tao told Yingke that the market size of robots in the domestic automobile production line is about 100 billion yuan. The company plans to first focus on the automobile scenario and then extend to a wider range of industrial scenarios in 3 to 5 years.

The following is an interview between Yingke and Zhang Tao (slightly edited):

Yingke: Previously, many embodied intelligence companies tried to enter the automobile scenario but failed. There was even a voice in the industry that "the automobile scenario has been falsified". Why did you choose to focus on the automobile scenario in the opposite direction?

Zhang Tao: The failure of robots to enter the automobile scenario is not because it is falsified, but because they are "not ready". Most companies that entered the automobile scenario in the early stage first told stories and then adapted products, lacking an understanding of the scenario process. Take a certain international luxury automobile manufacturer as an example. A certain humanoid company it cooperated with before used a biped + dexterous hand solution for welding and loading. Due to walking vibrations, the workpiece drop - off rate was 80%. Eventually, it was forced to mount an AGV to barely make it work. This is essentially because the body design did not match the scenario requirements.

Automobile manufacturing is the earliest test - ground for new industrial technologies. PLCs, robotic arms, and AI quality inspection all originated from here. And the problems that can be solved by existing technologies have already been covered by automation. Embodied robots should precisely tackle those "remaining 30% digital gaps".

Yingke: What is the core entry point for embodied robots in the automobile scenario?

Zhang Tao: It is not to replace the 90% of mature robotic arm workstations, but to solve three major types of "automation blind spots": First, scenarios with occupational health risks, such as the risk of burns in welding, loading, and unloading, and the damage to the cervical and lumbar spines in high/medium/low - position assembly;

Second, scenarios with quality consistency requirements, such as tail - line quality inspection, where fixed cameras cannot flexibly adjust the angle and the recall rate of AI quality inspection is insufficient;

Third, flexible production scenarios (such as the dynamic adjustment requirements in multi - model mixed - line production). Our Phi - Bot X1 has achieved zero errors in continuous 21.5 - hour welding, loading, and unloading, and increased the mobile quality inspection efficiency by 25% - 45% compared with manual work.

Yingke: What is the difference between your so - called "physical - native" and the current mainstream VLA and world models?

Zhang Tao: VLA is essentially "action mapping" — after pre - training a VLM, it grafts action experts and learns by fitting human demonstration data. It needs to be retrained when the task or the position of the sensor changes, with weak generalization ability. The world model mainly focuses on pixel - level world prediction and has limited understanding of the underlying physical laws, especially the causal relationship between world changes and behaviors. The core of our physical - native model is to enable robots to independently master physical causal laws through physical interaction.

For example, in the scenario of hole - alignment during loading and unloading, there is no need to collect a large amount of data on "how to adjust when it is placed crookedly". Instead, the robot can conduct parallel trial - and - error in high - fidelity simulation and independently learn "in which direction to adjust to align when it is stuck". One graphics card can virtualize 1000 robots for synchronous iteration, with a cost far lower than collecting data with real machines.

Yingke: How do you view automobile enterprises' self - development of robots? Will it compete with you?

Zhang Tao: If the goal of automobile enterprises' self - development of robots is to "enter their own production lines", the business logic is not valid. The number of production lines of an automobile enterprise is limited, and the solutions developed by one enterprise will not be used by other enterprises, and the data cannot circulate across factories.

It is more likely that Tesla and XPeng are developing robots for the C - end home scenario, which is complementary to our B - end industrial positioning. The advantage of automobile enterprises lies in manufacturing and channels, but embodied intelligence requires a change in the technical paradigm. It is difficult for the traditional Tier1 supply model to develop core algorithm capabilities. Referring to autonomous driving, the self - development results of most automobile enterprises are not ideal.

Yingke: Is the hardware of Guangxiang self - developed? Will hardware become a barrier in the future?

Zhang Tao: In the early stage, it adopts a model of "self - developed design + OEM integration by external suppliers", and will gradually take back the core hardware capabilities later. But in the long run, after the Chinese supply chain matures, the hardware will quickly become equal.

Yingke: Will you enter the To C market in the future? What is the rhythm of your listing plan?

Zhang Tao: The To C market will be considered at least 3 - 5 years later. There is a listing plan, but it is not the core goal at this stage. The current focus is on polishing products and implementing scenarios. We are more concerned about how many sold robots are in active use because only continuous feedback from real scenarios can form a product closed - loop, which is the core competitiveness of an embodied company.

Yingke: What stage is the industry currently in? Is there a bubble?

Zhang Tao: 2026 is the inflection point from "watching demos" to "watching implementation". In the next 1 - 2 years, the implementation data will determine the direction of the industry. There is indeed a phenomenon of "false implementation" at present, but it will eventually return to the essence of business.

We do not pursue short - term high valuations. The company was officially established in April 2025. In the early stage, we spent two or three months in automobile factories to investigate the process, let the robots start working on the production line, and then launched the products. Embodied intelligence is a 10 - or 20 - year track, and it doesn't matter whether it is high - profile in the first year.

Homepage image source | Provided by the enterprise

Typesetting | Fan Xinya

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