2024 was hailed by the industry as the “first year of embodied AI.” Dozens of robotics startups received funding, and the combination of large models and robotics became a hot topic. Yet as we enter 2025, a harsh reality has emerged: the iteration speed of algorithms far exceeds the accumulation speed of data, and data has become the biggest bottleneck in the development of embodied AI.
“Data Hunger” Spreads Across the Industry
A study from Stanford University shows that over 80% of current embodied AI projects are delayed due to data quality issues. Unlike natural language processing, the data required for embodied AI is not text, not images—it is multimodal “perception-action” data generated through real interaction between machines and the physical environment.
This type of data has several unique characteristics:
Scarcity: Unlike the inexhaustible supply of text and images on the internet, physical interaction data requires real hardware, real scenes, and real time. A failed grasp requires the robot to reset before attempting again. Data acquisition costs are hundreds of times higher than image data.
High Dimensionality: A simple grasping action involves temporal alignment of multiple modalities—visual images (30 fps), tactile matrices (1000 samples per second), joint torque (500 samples per second), acceleration, and more. A single interaction can generate gigabytes of data.
Non-Simulability: Despite increasingly powerful physics engines, the subtle physical characteristics of the real world—the friction difference between rubber and metal, the tactile variation of paper under different humidity levels, the micro-vibrations of an object at the moment of slipping—remain impossible to simulate perfectly. Models trained purely on synthetic data often fail in real environments.
VISME’s Data-First Strategy
Facing this industry dilemma, VISME chose a “data-first” technical path from the very beginning. We believe that in the field of embodied AI, data is not a byproduct of algorithms, but a strategic asset that determines the upper limit of intelligence.
Our strategy has three pillars:
First, build a data moat. Through proprietary tactile sensors and large-scale data collection scenarios, VISME is accumulating the world’s largest dataset of multimodal real interaction data. This data covers not only common grasping actions but also complex scenarios such as fine manipulation, dynamic interaction, and multi-object operations, creating a data barrier that is difficult to replicate.
Second, define data standards. The current embodied AI field lacks unified data formats and annotation standards, with each player operating in isolation—hindering data circulation and reuse. VISME is collaborating with industry partners to promote the establishment of standards for “vision-perception-action” data acquisition and annotation, making data assets truly reusable and tradable.
Third, foster an open data ecosystem. We believe that the prosperity of embodied AI requires an open data ecosystem. While protecting commercial confidentiality, VISME will gradually open portions of its datasets to academia and the developer community to jointly advance technological progress.
2025: The Year of “Data Players”
If 2024 was the “year of algorithm狂欢” for embodied AI, then 2025 will be the “year of data沉淀”—a return to rationality. Companies that can efficiently collect, clean, and annotate real physical data will establish an unshakable first-mover advantage in the competition over the next two years.
VISME is ready. Because we believe that on the long road of intelligence evolution, data is not fuel—it is soil. Algorithms grow upon it, models bloom from it, and the fruits that are ultimately harvested belong to those who first tilled this soil.
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