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Hesai launched Kosmo on October 14, 2026, as an enterprise system for converting real environments into editable 3D training worlds. It combines the Kosmo One spatial camera with the Kosmo World cloud platform, which reconstructs scans as 3D Gaussian Splatting (3DGS), mesh and depth data, then prepares those assets for robot simulation, world-model development and immersive production.
The company’s central claim is that developers can capture a physical space once and generate many more usable training situations than they could by manually modeling or repeatedly photographing the same location. Hesai has announced partnerships with embodied-AI companies Galbot and Dexmal, but has not published a retail price, consumer shipping plan or Amazon listing for Kosmo One or Kosmo World.
What is Hesai Kosmo?
Kosmo is not a standalone camera or a consumer scanning app. It is an enterprise spatial-data stack with three connected parts: a capture device, reconstruction software and cloud services. Kosmo One records a room or outdoor environment; Kosmo World turns that capture into reusable 3D assets and simulation-ready data; robotics and media teams then use the outputs in their own development pipelines.
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Hesai positions the system for embodied-AI training, robot world-model development and immersive content creation. That makes it relevant to humanoid-robot companies, simulation teams, film and visual-effects studios, and organizations that need large amounts of real-world spatial data without building every environment by hand.
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How Kosmo turns a scan into robot-training data
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Capture geometry, appearance and motion
Kosmo One combines high-resolution lidar, a one-inch imaging sensor and a high-precision inertial measurement unit. Hesai says the sensors are synchronized at the microsecond level, allowing the system to associate geometry, color, texture and camera position in the same capture.
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Reconstruct the environment
Kosmo World combines 3DGS, mesh and depth representations rather than forcing a project to use only one format. Hesai describes the resulting reconstruction as having millimeter-level geometric accuracy and lifelike visual detail. The company does not publish an independent benchmark establishing how that accuracy compares with other capture systems.
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Complete and label the scene
The platform can use generative filling for areas that were not scanned, reconstruct materials and textures, and apply semantic segmentation. These operations turn a raw point-and-image capture into a structured environment in which objects and surfaces can be identified and manipulated.
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Adapt the world for a robot
Kosmo World can adapt views to a robot’s camera characteristics. That step is important because a simulated scene must resemble what a particular robot’s sensors would actually see, rather than simply looking convincing to a human viewer.
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Generate training and simulation content
The reconstructed space can be connected to robotics and content tools, where teams create trajectories, vary conditions and produce data for policy training or world-model experiments. Hesai says one captured space can yield more than 10,000 training trajectories.
What is Kosmo One?
Kosmo One is the physical spatial camera in the system. Its sensor combination is intended to preserve both precise structure and realistic appearance: lidar supplies three-dimensional geometry, the one-inch camera records visual information, and the inertial measurement unit tracks motion and pose. Microsecond-level synchronization is designed to reduce alignment errors between those streams during movement.
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Hesai says a typical 100-square-meter indoor space takes approximately 15 minutes to scan. The figure is not a fixed guarantee: the company says layout, total size and capture requirements change the actual time. Public launch materials describe Kosmo One as part of an enterprise offering and do not disclose a retail price, shipping geography or consumer purchasing channel.
Published performance figures
The following numbers come from Hesai’s 2026 launch materials and investor communications. They are company-reported results, estimates or comparisons rather than independent tests.
| Claim | Published figure | Qualification |
|---|---|---|
| Scan of a typical indoor space | Approximately 15 minutes for 100 square meters | Hesai says time varies with layout, size and capture requirements. |
| 3DGS model-generation speed | Up to approximately 5× faster than comparable products | Comparison assumes similar hardware, dataset and quality requirements; results vary by scene, data volume and parameters. |
| Dairy Queen deployment | About 300 hours of prior manual modeling versus 15 minutes of scanning and reconstruction | Deployment example supplied by Hesai. |
| Sharpa robot task success | 99% | Hesai says the failure rate fell to one-tenth of its previous level; this is not independent testing. |
| Training-data output | More than 10,000 trajectories from one captured space | Hesai says this represented more than 100× acquisition efficiency at the same cost under its comparison methodology. |
| European streetscape example | 10,000 square meters scanned, reconstructed and edited in about one week | Hesai estimates a physical-set build would take about two months and that savings could reach 80% in typical scenes. |
These figures indicate the intended economic benefit: replacing repeated manual scene construction with a capture-and-reuse workflow. They do not establish that every site will achieve the same scan time, fidelity, trajectory count or cost reduction.
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Robotics partners and software integrations
Named launch partners
Hesai announced strategic partnerships with Galbot and Dexmal, both described as embodied-AI companies. Its second-quarter 2026 investor release said prototypes had been delivered in July, orders had been secured from leading humanoid-robotics companies including Galbot, and more than 200 prospective partners had been engaged across industries.
Simulation and content tools
Hesai lists integrations with NVIDIA Isaac Sim, NVIDIA Isaac Lab, Unreal Engine, Pixar RenderMan and Houdini. Those connections cover different stages of a workflow: robotics teams can use simulation and learning environments, while media teams can render or edit reconstructed worlds. Demonstrations also involved Pixar RenderMan and Amap.
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An integration listing does not by itself specify the depth of support, file formats, licensing terms or whether every feature was generally available at launch. Teams evaluating Kosmo should confirm the supported versions and export path for their target software.
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How the business is expected to work
Hesai describes two commercial models. The first is an integrated hardware-and-software solution for robotics and media customers. The second is a service that produces real-world spatial data at scale for world-model and Physical-AI developers. This allows a customer either to operate the capture and processing workflow or to commission spatial-data production as a service.
Hesai’s investor materials said initial Kosmo revenue was expected in the third quarter of 2026. That statement is a company expectation, not a published revenue result. The launch materials do not state a standard subscription, per-scan fee, hardware list price or public ordering process.
What Kosmo changes for robot development
Traditional robot training environments can require manual 3D modeling, repeated photography, physical mock-ups or extensive simulation engineering. Kosmo’s proposed alternative is to start with a real location, preserve its geometry and appearance, and then create many controlled variations in software. A restaurant, streetscape or warehouse could therefore become a repeatable training environment instead of a one-time backdrop.
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Limits and questions buyers should resolve
- Independent validation: Hesai has supplied the published speed, accuracy and deployment figures; no independent benchmark is established by the launch materials.
- Scene variability: Scan time and reconstruction speed depend on layout, data volume, hardware, quality settings and other parameters.
- Unscanned areas: Generative filling can complete missing regions, but teams should distinguish inferred content from directly captured geometry when safety or measurement accuracy matters.
- Commercial terms: Public materials do not disclose price, shipping geography, retail availability or an Amazon listing.
- Integration details: Customers should verify supported versions, APIs, export formats, cloud requirements, data retention and any software licenses before committing to a production pipeline.
Bottom line for prospective users
Kosmo is best understood as an enterprise pipeline for turning physical places into reusable digital training environments. Kosmo One captures synchronized lidar, imagery and motion; Kosmo World reconstructs and enriches the result; robotics and content tools consume the resulting 3DGS, mesh, depth and labeled data. Galbot and Dexmal are the named robotics partners, while NVIDIA Isaac Sim, Isaac Lab, Unreal Engine, Pixar RenderMan and Houdini are listed integrations. The technology’s strongest evidence so far is Hesai’s own deployment and efficiency reporting, so buyers should treat the headline percentages as claims to validate against their scenes, robots and software stack.
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