China’s Hesai Unveils 3D Spatial Camera to Fuel Physical AI Robotics
Discover Hesai Group's new 3D spatial camera designed to capture physical spatial data and power advanced AI training for humanoid robotics.
POLICY WIRE — Shanghai, China — Autonomous driving technology enterprise Hesai Group officially introduced a novel three-dimensional mapping camera on Wednesday, aiming to supply critical physical spatial data required for training artificial intelligence systems in humanoid robotics.
Recognized globally as the top manufacturer of vehicle lidar sensors, the firm is branching out into physical artificial intelligence to secure fresh avenues for corporate growth.
Driven by the company’s proprietary Kosmo spatial intelligence platform, the newly launched hardware merges lidar, high-definition imaging, and 3D Gaussian Splatting capabilities to construct detailed, interactive, and editable three-dimensional models.
This technology allows operators to navigate and analyze digital environments from multiple perspectives.
To overcome existing data bottlenecks, Hesai engineered the Kosmo ecosystem, which combines a specialized AI spatial camera, advanced algorithms, three-dimensional assets, and cloud infrastructure.
Initially introduced as an AI spatial intelligence tool in April, the Kosmo system is scheduled for a full commercial rollout in the second half of 2026.
The strategic deployment of the device is designed to satisfy surging industry requirements for premium spatial data, accelerate artificial intelligence robotics capabilities, and establish the organization as a premier supplier within the robotics sector.
Described as a pocket-sized capture device, the hardware records real-world physical environments to produce high-value spatial assets for training physical artificial intelligence world models.
Alongside this release, Hesai is introducing robotics power modules, marking a strategic expansion from mere sensory input hardware into comprehensive physical execution systems.
Reporting by Policy-Wire (PW)




