DataMesh Robotics helps robot makers and robotics teams generate industrial-grade training data from physics-based digital twins—including photoreal visuals, sensor simulation, and automatic ground-truth labeling. Build robust models faster, iterate safely, and deploy with higher confidence in real-world industrial environments.
Build high-fidelity industrial environments from CAD/BIM, facility drawings, asset libraries, and site constraints—optimized for simulation at scale.
Generate high-quality RGB and synthetic imagery with controllable lighting, textures, and camera optics—supporting robust perception training across real-world variability.
Physics & Material Properties Assign physical attributes (mass, friction, restitution, joints, constraints) and material definitions to make interactions realistic—essential for manipulation, contact, and mobility learning.
Produce consistent annotations at scale: segmentation masks, 2D/3D bounding boxes, instance IDs, depth, optical flow*, keypoints*, poses, trajectories, and scene metadata.
Defining goals, success conditions, and reward signals for industrial tasks: tight tolerances, multi-step procedures, safety constraints, partial observability, and domain-specific semantics.
Package datasets and scenes for downstream training, evaluation, and sim-to-real workflows—including integration paths for NVIDIA Isaac Sim/Omniverse and common robotics toolchains.
We have built an early prototype and collaborated with enterprise partners including China Unicom to explore industrial/facility environments and simulation-based data generation workflows for robotics.
We can generate multi-modal datasets such as RGB images, depth, segmentation, instance IDs, 2D/3D bounding boxes, object poses, robot state/trajectories, and scenario metadata. Outputs are configurable to your training goals and target simulator.
Both. Perception datasets are common, but DataMesh Robotics is built for embodied tasks where physics matters—manipulation, contact-rich interactions, mobility, and inspection actions.
We combine industrial-accurate geometry and constraints with physics parameters and structured variation (domain randomization). We also support iterative scenario generation based on observed failure modes in validation.
DataMesh Robotics is designed to integrate with OpenUSD-based workflows and can be adapted to support Isaac Sim/Omniverse pipelines depending on your environment and requirements.
Yes. We can ingest your assets and help optimize them for simulation while supporting enterprise deployment options to protect IP.
A pilot commonly includes: one target environment, a small set of tasks, a defined dataset spec, an integration path to your training stack, and a performance validation loop. Timelines depend on asset readiness and complexity. We also have ready-to-use templates to generate generic training data in certain industry.
DataMesh Robotics suites can be used with both cloud and on-premise environments.