Recently, the field of artificial intelligence has set off another huge wave, and a news has aroused widespread attention and heated discussions throughout the industry: ISRLab of the School for Interdisciplinary Information Sciences of Tsinghua University and RobotEra have joined hands to open source the first AIGC robot large model VPP (Video Prediction Policy) in the ICML2025 Spotlight selection. This achievement is not only a major breakthrough in the field of academia and technology, but also a powerful engine to promote the commercialization process of robots. In this article, China Exportsemi will deeply analyze the technical highlights, performance advantages and far-reaching impact of the VPP model on the commercialization of robots, and discuss whether it can lead the robot industry to achieve a real "hurricane leap" based on detailed data and specific examples.
1. Emerging edges: technological breakthroughs and innovation highlights
The core innovation of the VPP model is to successfully graft the generalization ability of the video diffusion model into the general robot operation strategy, breaking the limitation that the previous robot strategy can only learn actions based on current observations. It gives the robot the ability to "predict the future", so that the robot knows the upcoming scenario before taking action, so as to be like an experienced chess player, every move "without regrets", which is undoubtedly a big step in the history of robotics development.
Through the ingenious design of the two-stage learning framework, VPP has achieved a qualitative leap in model performance. In the first stage, it uses the video diffusion model to learn predictive visual representation. In the second stage, Video Former and DiT diffusion strategies worked together to complete the action learning. In the Calvin ABC-D benchmark, this innovative architecture enabled VPP to achieve an average length of task completion of 4.33, which is just one step away from a perfect score of 5.0 and a performance improvement of up to 41.5% compared to previous technology. This means that the robot can complete the instructions in a more efficient and accurate way in the face of complex tasks, which lays a solid foundation for the wide application of the robot in practical scenarios.
2. Unique advantages: open up a new perspective on the development of robots
*Efficient data utilization: VPP uses massive Internet video data for training and directly learns human movements, so that the robot can draw knowledge from rich visual information. This not only reduces the over-reliance on high-quality robot data, but also greatly broadens the learning boundaries of robots. For example, in some complex gesture operations or scene-specific interaction tasks, VPP can quickly extract key action features from relevant videos to achieve efficient imitation and learning, which is unimaginable in previous models that rely only on real machine data training.
Figure: VPP helps robots further improve their learning capabilities
*Cross-ontology learning ability: VPP breaks through the data barriers between different robot ontologies and realizes the smooth flow of knowledge. It can directly learn video data from various forms of robots and human operations, whether it is a robotic arm of different shapes on an industrial production line or a humanoid robot in a home environment, VPP can easily cope with it. This cross-ontology versatility provides great convenience for enterprises to deploy robots in multiple models and scenarios, and reduces the time and cost spent on adapting to different ontologies.
Figure: VPP breaks through the data barriers between different robot ontologies and realizes the smooth flow of knowledge
*Explainability & Debugging Optimization: Unlike traditional end-to-end models, VPP's predictive visual representation has some interpretability. Instead of conducting large-scale, costly testing in the real world, developers can pinpoint potential problems, identify failure scenarios and tasks in advance, and debug them by analyzing the video predicted by the model. This not only greatly shortens the model optimization cycle, but also improves R&D efficiency, so that robot products can be brought to market faster.
3. Commercialization considerations: from technological breakthrough to market application
*Balance between cost and benefit: VPP reduces the dependence on high-quality real machine data and draws nutrients directly from Internet video, which undoubtedly reduces the cost of data collection and annotation. At the same time, its cross-ontology learning ability reduces the manpower and material resources invested in adapting and optimizing different robot ontologies. Taking a medium-sized robot manufacturing enterprise as an example, if the traditional technology route is adopted, it may need to invest millions or even tens of millions of yuan in data collection and model adaptation every year, and the application of VPP is expected to reduce the related costs by 30%-50%. The reduction of costs makes the price of robot products more competitive and creates favorable conditions for expanding market share.
*Market expansion and application scenario expansion: With its strong multi-task learning ability and generalization ability, VPP empowers robots to show their skills in many fields. In the field of industrial manufacturing, robots can more accurately complete complex tasks such as parts assembly and product inspection, and improve production efficiency and product quality. In the field of smart home, robots can skillfully carry out housework such as item handling and cleaning, and provide convenient services for consumers; In the field of logistics and distribution, robots can efficiently complete tasks such as warehouse management, cargo handling and distribution, and improve logistics efficiency. With the continuous expansion of application scenarios, the market demand for robots will also show explosive growth. According to market research institutions, the global robot market is expected to grow at an average annual rate of 25%-30% in the next five years, and the application of VPP will inject strong impetus into this growth.
* Reshaping the competitive landscape: The emergence of VPP will promote profound changes in the competitive landscape of the robot industry. Those companies that can take the lead in mastering and effectively applying VPP technology will take the lead in the market competition and are expected to quickly rise to become industry leaders. For those companies whose technology is slow to update and cannot adapt to market changes, they may face the risk of market share being eroded or even eliminated by the market. This will further accelerate the survival of the fittest in the industry and promote the development of the robot industry to a higher level.
4. Challenges and solutions
Although the VPP model shows great potential, there are still many challenges in its commercialization.
First and foremost, the safety and reliability of the model is a top priority. In the actual application scenarios of robots, once there is a prediction error or execution error, it may cause serious consequences, such as equipment damage in industrial production and safety hazards in smart homes. Therefore, the R&D team needs to continuously improve the model and improve its security and stability through a large number of simulation tests and real-world scenario verification.
Second, the balance between technology, policy, regulation, and ethics also needs to be addressed urgently. As robots become more intelligent and have more and more application scenarios, policy, regulation and ethical considerations cannot be ignored. For example, how to protect user privacy in the process of robot data collection and use; When robots make autonomous decisions, how to ensure that their decisions are in line with human values and social ethics. Enterprises and scientific research institutions need to work together with government departments and all sectors of society to jointly formulate relevant policies, regulations and ethical guidelines to create a good environment for the healthy development of robots.
Finally, public awareness and acceptance are also important factors affecting the commercialization of robots. Some consumers may have doubts about the safety and reliability of robots, and are cautious about the use and popularization of robots. This requires enterprises to strengthen publicity and promotion, improve the public's awareness and understanding of robots by holding product experience activities, carrying out popular science education, etc., and enhance consumers' trust and acceptance of robots.
5. Conclusion: The future has come, and VPP has opened a new era of robots
The advent of the VPP model is undoubtedly a milestone in the field of robotics. With a performance improvement rate of 41.5%, it paves a new path for the commercialization of robots with a series of innovative technologies and unique advantages. In the situation of coexistence of opportunities and challenges, we have reason to believe that with the continuous progress and improvement of technology, the increasingly sound policies and regulations, and the gradual improvement of public awareness, VPP will lead the robot industry to achieve from "steady progress" to "rapid progress" of the leap. In the near future, robots are expected to be deeply integrated into people's daily life and work like smartphones, bringing unprecedented changes and opportunities to the development of human society. Let's look forward to and witness this exciting journey together!
