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Livn VR
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Robot Fleet
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Livn VR
Home
Worlds
Robot Fleet
About Us
Contact
Home
Worlds
Robot Fleet
About Us
Contact

The Portal Between Spatial Reality And Autonomy

Before and Autonomous robot ever meets a real-world factory floor, storefront, or farm, it needs to learn how to navigate reality. Livn VR provides high-fidelity, photorealistic spatial twins that allow robotic developers to stress-test navigation, vision, and manipulation algorithms in a zero-risk digital environment. We don’t just Build 3D models, we build the ground truth for Physical intelligence

‍ Use Cases

  • Retail & Store fronts: Digitize product catalogs, design virtual store layouts, and create interactive shopping experiences with true-to-life product representations.

  • Factories & Warehouse: Simulate entire factory floors, optimize robot paths, and test new production lines before a single piece of hardware is deployed.

  • Crop layout & Farming Planning: Create immersive digital twins of farms and leverage crop plans for adequate spacing for crop growth, for indoor and outdoor.

Architecture for Immersive Physical Intelligence (AiPi)

  • Precision Spatial Capture: Sub-millimeter foundational advanced LiDAR and photogrammetry to generate high-resolution geometric data and wire frames. This creates a digital foundation, ensuring that every asset; from micro-components to entire industrial complexes; is mapped with absolute geometric and visual accuracy.

  • Omniverse-Powered Interoperability: By leveraging NVIDIA Omniverse, we unify fragmented 3D data sets into a cohesive, persistent digital asset library based on Universal scene description. Our DeepSearch framework within Nvidia Omniverse enables you to seamless search assets with natural language or an image enabling collaboration and real-time synchronization between multiple projects and users. The developer can drag and drop USDs from DeepSearch into Isacc Sim within couple of seconds and test their robots.

  • Neural Asset Reconstruction: Our AI turns messy, complex raw scan data into clean, high-quality optimize 3D assets. We make these models lightweight, so they run smoothly on your computer, without losing any of the detail or accuracy needed for physical intelligence.

  • World Simulation: We develop virtual environments that reduce the sim2real gap. We don’t create worlds that just look real, they behave real. We simulate materials, lighting, physics, and dynamic objects to validate AI models before they are released into the real world. This enables users of Livn VR to stress-test workflows, simulate machinery behavior, and validate architectural changes in a risk-free, predictive digital landscape.

Using Agents To Go From Raw Data to "Ground Truth" In Sim

Raw scan data is dense and often difficult for standard software to process. This is where the open Physical AI model’s like SPARSE4D, SAM3D, and DET 3D are leveraged to process raw scan data to identify dynamic objects in the scan and deliver a python script with meta data that can be used by DeepSearch to place assets in a USD scene using an agent.

Livn VR agent uses 3D semantic segmentation to identify walls, pillars, screens, safety

The Digital Twin as a Robotic Sandbox

Traditional robot integration is often plagued by "integration fatigue"—where a robot is installed on the floor, only to find that it conflicts with existing machinery or workflow patterns.

  • Virtual Commissioning: Using our 3D scans, we build a perfect digital twin of your facility. Robotics engineers can then import their robot models into this virtual space to verify reach, collision-free paths, and cycle times in a risk-free environment.

  • Physics-Based Validation: Since our digital twins are physics-consistent, the robot’s movement in the virtual world mirrors its physical capability. If a robot reaches for a part in our simulation, it will do the same in your factory.

  • Network Camera Validation: You can test, validate and commission security cameras virtually to validate AI models in sim before deploying on the shop floor.

World Model AI Training

Physical AI requires massive amounts of data, which is difficult to collect from a single robot on a busy floor. We provide a tool chain that enables a developer to collect large amount of 3D data in simulation. We use the following techniques to optimize robots in the real world, like AMR, Manipulator, quadruped, and humanoids.

  • Synthetic Data Generation: We can run thousands of variations of a task in the virtual world (e.g., picking up parts in different orientations) to "train" the robot’s AI.

  • Optimized Kinematics: By analyzing the virtual environment, our system suggests the most efficient movement paths for robots, reducing mechanical wear and increasing the speed of the task. We are currently testing new libraries for humanoids motion generation like NVIDIA MotionBricks.

  • Path planning and collision prediction: We generate thousands of paths for robot to predict collisions that might occur in sim in order to operate the robot safely in real world. We generate small video example to fine tune world model by showing causation in simulation.