Category: Visual Embodied Intelligence

The history of artificial intelligence is a history of data. Every major breakthrough has been enabled by a new source of data at unprecedented scale. ImageNet provided the million-image dataset that fueled the deep learning revolution in computer vision. Common Crawl and The Pile provided the web-scale text corpora that enabled large language models. YouTube…

Simulation has transformed robotics research. Physics engines like MuJoCo, PyBullet, and Isaac Sim enable researchers to train robots in virtual environments at speeds impossible in the real world. A robot can accumulate years of experience in days, learning to walk, grasp, and navigate without wearing out hardware or endangering itself. But when these simulation-trained robots…

Industrial robotics has excelled at handling rigid objects—metal parts, plastic components, assembled products with fixed shapes and known properties. The rules are simple: position, orientation, geometry are all that matter. Pick, place, insert, fasten. But the world is not rigid. It is full of soft, deformable, unpredictable objects: clothing, food, packaging, bedding, cables, plants, human…

Vision has dominated the robotics perception landscape for decades. Cameras are cheap, ubiquitous, and increasingly capable. Vision-based algorithms have enabled remarkable achievements in navigation, object detection, and scene understanding. But for manipulation—physically interacting with objects—vision alone is fundamentally insufficient. The Limits of Vision Consider a seemingly simple task: picking up a coffee mug by its…

In the race to build general-purpose robots, most attention focuses on algorithms, models, and hardware. But beneath the surface, a quieter battle is being waged—one that will ultimately determine the industry’s long-term winners and losers. The data pipeline is becoming the new moat. Unlike internet AI, where models are trained on publicly available text and…

In March 2025, VISME officially launched VISME Data Hub—a professional data service platform tailored for embodied AI developers. It is the world’s first one-stop platform dedicated to “vision-perception-action” multimodal real interaction data. Core Platform Features 1. Massive Real-World Datasets VISME Data Hub’s initial release includes 500,000 real-world grasping and manipulation samples, covering: All data is collected…

When ChatGPT burst onto the scene in late 2022, it sparked a global AI frenzy. People began to wonder: when will robotics have its “ChatGPT moment”—a general-purpose model that can understand the physical world and perform various tasks? More than two years later, that moment has yet to arrive. Why? Root Cause: Fundamentally Different Data…

Industrial robots have existed for decades, but most of them are “blind”—repeating pre-programmed trajectories with no awareness of their environment or variations in workpieces. When a workpiece position shifts slightly, when surface material differs somewhat, when assembly force needs fine-tuning—traditional industrial robots are at a loss. VISME’s multimodal real interaction data is changing this reality.…

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…

In the field of embodied AI, there is a classic challenge known as “hand-eye coordination.” When a robot attempts to grasp an object, the visual system tells it “the object is there,” and the motion system commands the arm to “move toward it.” But a critical component is missing between the two: tactile feedback. When a…