Recognised as an Empresa Emergente under Spain’s Startup Law
At ImmersiVERSE, we develop simulation-driven technologies for Physical AI, robotics, and autonomous systems, combining physics-based digital twins, realistic sensor simulation, synthetic data generation, AI training, reinforcement learning, and XR teleoperation. Our projects span intelligent robots, autonomous vehicles, UAVs and UGVs, industrial environments, and challenging real-world conditions such as rain, fog, smoke, wind, and night, enabling AI systems to be trained, tested, and validated in realistic virtual environments before deployment in the physical world.
We build and research physics-based mathematical models that capture the complexity of real-world systems.
Use mathematical models to reproduce realistic LiDAR, radar, and camera measurements, including noise, range, resolution, and sensor-specific behaviour
Model rain, fog, snow, and atmospheric effects mathematically to simulate their impact on sensor signals, detection probability, and measurement uncertainty.
Generate physically consistent sensor outputs and ground-truth data enabling scalable AI training, validation, and perception testing.
Apply physics-based mathematical models for vehicle motion, including acceleration, steering, braking, tire forces, and 6-DOF dynamics.
Physics-aware agents that perceive, decide, and act through realistic forces, constraints, and sensor feedback.
High-fidelity virtual replicas of real-world assets or environments used for analysis, optimization, and continuous validation
Integration of Gaussian splats, sensor data, and trajectories from deployed systems to ground simulation in reality
Scalable creation of labeled data, including rare, unsafe, or hard-to-capture scenarios.
Systematic variation of physics, visuals, and parameters to improve robustness and generalization for policies
Emulating sensors like cameras, LiDAR, radar, or IMUs to train and test AI perception modules in different and challenging edge scenerios
Enabling the creation of precise dataset of millions of images in a single day, revolutionizing the pace of AI development
Iterate and refine AI vision models, streamlining the development process for quicker deployment and optimal performance.
Reduces costs by enabling AI vision systems to train and optimize in a virtual environment, sparing expensive real-world data collection and hardware expense
we ensure that your sensitive information remains protected without compromise.
Ensures safety for AI vision systems by allowing them to learn and adapt in controlled virtual environments, minimizing the risk associated with real-world experimentation and potential harm
our simulation-driven approach empowers AI vision systems to effortlessly adapt to evolving demands, ensuring seamless integration and robust performance at any scale