As the hype around generative AI gradually subsides, embodied intelligence has become the most certain growth track for the AI industry in 2026. Unlike disembodied intelligence focused on virtual scenarios, embodied models aim to enable intelligent agents to “step into the physical world.” The realization of this goal hinges on the advancement of world models—only by enabling models to truly understand physical rules, dynamic scenes, and interaction logic can robots possess human-like decision-making capabilities. Today, the innovative combination of “UMI data collection + video learning” is breaking through the core bottlenecks in world model training, ushering embodied models into a new phase of scaled iteration and reshaping the global AI industry competitive landscape. This article, based on frontline industry practices and authoritative technical insights, analyzes the innovative value and implementation breakthroughs of this combination.

I. Under the Wave: Why World Models Have Become the “Deciding Factor” for Embodied Intelligence

The ultimate goal of embodied intelligence is to enable intelligent agents (robots, smart devices, etc.) to autonomously complete tasks in unknown physical environments. The core support behind this is the world model. If embodied models are the “limbs” of an intelligent agent, responsible for executing actions and perceiving the environment, then the world model is the “brain,” responsible for parsing scenes, reasoning about causality, and planning paths.

1. Traditional Dilemma: Two “Bottleneck” Challenges in World Model Training

For a long time, the development of world models has been constrained by two core pain points, preventing embodied models from moving beyond laboratory settings. First, there is a scarcity of high-quality interactive data. Traditional data collection relies on manual teleoperation, which is not only inefficient and costly but also limited in the range of scenarios covered, failing to meet the needs of scaled model training. Second, there is insufficient understanding of dynamic scenes. Relying solely on static data or isolated action data, models cannot capture the temporal dimension of the physical world, struggle to understand the causal relationships between actions and outcomes, and are prone to “failure” in complex scenarios.

Industry experts point out that previous iterations of embodied models largely remained at the level of “single-scenario adaptation,” essentially due to the insufficient cognitive capabilities of world models—they cannot construct general representations of the physical world and can only be custom-trained for specific scenarios. This has also led to slow progress in the industrial application of embodied intelligence, making scaled replication difficult.

2. The Key to Breaking Through: “Two-Way Empowerment” of UMI Data Collection and Video Learning

With the maturation of UMI (Universal Model Imitation) data collection technology and the iteration of video learning algorithms, the training challenges of world models are finding solutions. The deep integration of the two is not a simple technical addition but forms a closed loop of “data supply – cognitive upgrade”: UMI data collection addresses the question of “where does the data come from,” providing scaled, high-quality interactive data; video learning addresses “how to use the data,” mining causal logic in dynamic scenes and transforming data into the model’s “cognitive ability.”

The emergence of this combination has fundamentally changed the training paradigm for world models, shifting embodied models from “customized adaptation” to “generalized iteration.” It propels embodied intelligence from the laboratory into various industries, becoming the core driver of the current wave of embodied model updates.

II. UMI Data Collection: Reconstructing the Data Supply System to Give World Models “Material to Learn From”

The core value of UMI data collection technology lies in breaking through the bottlenecks of traditional data collection, building an efficient, low-cost, and scalable data supply system, and providing ample “training nourishment” for world models. Compared to traditional teleoperation collection, UMI data collection achieves a leap from “human-driven” to “intelligent collection,” becoming the “infrastructure” for the industrialization of embodied intelligence.

1. Technological Innovation: Three Breakthroughs Overcoming Data Collection Challenges

Compared to traditional data collection methods, UMI data collection achieves comprehensive breakthroughs in efficiency, cost, and universality. First, efficiency is multiplied. By combining imitation learning algorithms with automated collection devices, UMI data collection reduces the time required to collect a single interactive data point to one-fifth of traditional methods, significantly improving data collection efficiency. Second, costs plummet. Without the need for professional human operators to be constantly present, it enables 24/7 automated collection, reducing overall costs to less than one-tenth of traditional teleoperation, and even to one-hundredth in some scenarios. Third, universality improves. It decouples data from specific devices, allowing adaptation to different types and brands of embodied intelligence devices, breaking data silos, and enabling cross-device data reuse.

Taking a leading domestic UMI technology company as an example, its developed UMI collection system has achieved multi-scenario adaptation, covering fields such as industrial sorting, home services, and medical assistance. The accuracy rate for single data point collection reaches 99.2%, effectively solving the problem of “uneven quality” in traditional data and providing high-quality training material for world models.

2. Industrial Value: Activating the Data Ecosystem and Lowering Industry Entry Barriers

The proliferation of UMI data collection technology not only solves the data supply problem but also activates the entire embodied intelligence data ecosystem. Previously, due to excessively high data collection costs, most small and medium-sized enterprises (SMEs) and startups found it difficult to enter the embodied intelligence field, with industry resources concentrated in the hands of a few tech giants. The low-cost advantage of UMI data collection allows SMEs to also access high-quality interactive data, significantly lowering the R&D threshold for embodied models and world models.

Data shows that since 2026, the number of new domestic startups related to embodied intelligence has increased by 67% year-over-year, with over 80% of these companies using UMI data collection technology to obtain training data. Scaled data supply has also dramatically shortened the training cycle for world models—from several months traditionally to several weeks—accelerating the iteration speed of embodied models.

III. Video Learning: Endowing World Models with “Dynamic Cognitive Ability” to Let Intelligent Agents “Understand the World”

If UMI data collection provides the “raw material” for world models, then video learning is the key to transforming that material into “capability.” The essence of the physical world is dynamic change—the movement of objects, scene transitions, and action correlations all require parsing through temporal information, which video learning is adept at capturing.

1. Technical Core: From “Recognition” to “Reasoning,” Reconstructing Model Cognitive Logic

Video learning for world models has long moved beyond traditional “object recognition and action classification.” Its core lies in mining “causal relationships” and “pattern features” in dynamic scenes. By analyzing vast amounts of video data, models can learn the fundamental rules of the physical world—such as the gravitational properties of objects and collision dynamics—and understand the intent and logic behind human actions—for example, “picking up a cup” is to “drink water,” and “pushing open a door” is to “enter a room.”

Currently, the mainstream technical approach involves jointly training models with UMI-collected action data and video data: UMI data provides “standard action patterns,” while video data provides “scene context information.” Their combination enables the model to master standardized interactive actions while understanding the environmental context of those actions, thereby achieving a complete decision-making loop of “scene perception – causal reasoning – action planning.”

2. Implementation Breakthroughs: Flourishing in Multiple Scenarios, Validating Practical Value

With the deep integration of video learning and UMI data collection, the cognitive capabilities of world models have significantly improved, driving the implementation of embodied models across multiple industries. In the industrial sector, robots equipped with upgraded world models can autonomously adjust sorting actions by analyzing real-time assembly line video feeds, adapting to products of different specifications, increasing sorting efficiency by over 35%, and reducing error rates to below 0.5%. In the home service sector, smart robots can understand human habits through video learning and autonomously perform tasks like sweeping, window cleaning, and tidying items, responding to dynamic changes in household scenes. In the medical field, assistive robots can analyze surgical videos and operation data to help doctors perform simple surgical procedures, enhancing surgical efficiency and safety.

Furthermore, in fields like autonomous driving and warehouse logistics, the combination of “UMI data collection + video learning” is accelerating deployment, continuously improving the adaptability and decision-making capabilities of embodied agents, and gradually moving toward the goal of “generalization and intelligence.”

IV. A Promising Future: Continuous Technological Iteration, World Models Moving Toward “Generalization”

The combination of “UMI data collection + video learning” not only drives the rapid iteration of current embodied models but also charts the course for the future development of world models. As technology continues to optimize, their synergistic effects will be further unleashed, propelling world models from “specialization” toward “generalization,” enabling embodied intelligence to truly integrate into human life.

1. Directions for Technological Iteration: More Efficient, More Accurate, More General

In the future, UMI data collection technology will evolve toward “greater efficiency and accuracy,” further reducing data collection costs, expanding the breadth and depth of covered scenarios, and enabling real-time data collection and transmission to meet the needs of real-time model training. Video learning algorithms will advance toward “more precise causal reasoning,” combining with large language model technology to enable models to quickly parse multi-dimensional information in complex scenes, enhancing the flexibility and accuracy of decision-making.

Moreover, the fusion of the two will deepen, achieving full-process automation of “data collection – model training – scenario validation,” further shortening the iteration cycle of embodied models and accelerating industry development.

2. Industrial Impact: Reshaping the AI Competitive Landscape and Spurring New Sector Opportunities

As “UMI data collection + video learning” drives the upgrade of world models, the industrialization of embodied intelligence will further accelerate, spurring more new sector opportunities—from UMI collection devices and video analysis algorithms to general-purpose embodied robots and industry-specific customized solutions—forming a complete industrial ecosystem. Simultaneously, the maturity of this technological path will give China a first-mover advantage in the field of embodied intelligence, breaking foreign technological monopolies and reshaping the global AI industry competitive landscape.

Conclusion: Ushering in a New Era of Embodied Intelligence with a Technological Combination

The wave of updates in embodied models is, in essence, a wave of upgrades in world models; and the breakthroughs in world models are inseparable from the synergistic empowerment of “UMI data collection + video learning.” This innovative combination breaks through the core bottlenecks that have long plagued the industry, moving embodied intelligence from a “laboratory dream” to an “industrial reality” and injecting new vitality into the development of the AI industry.

In 2026, with the continuous iteration of technology and the ever-expanding implementation scenarios, “UMI data collection + video learning” will become the universal paradigm for world model training, propelling embodied intelligence into a golden age of scaled development. For enterprises, seizing this technological dividend and investing in related fields will enable them to seize opportunities in this new wave of AI industrial transformation. For the entire industry, the proliferation of this combination will drive AI technology to truly enter the physical world, bringing about more profound changes to human life.