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Generate exponentially large amounts of synthetic motion trajectories for robot manipulation from just a few human demonstrations.RoboticsEnterpriseSynthetic Manipulation Motion Generation for Robotics
Use when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline. Use when the user wants to set up, scale, validate, or harden NVIDIA physical AI infrastructure for synthetic data generation workflows across local MicroK8s or Azure AKS, including Kubernetes clusters, inference endpoint deployment, OSMO deployment, work Use when running the CDF AnomalyGen pipeline over a defect dataset — mask placement, fine-tuning, synthetic defect-image generation (SDG), evaluation, quality refinement, and pseudo-labeling — even when the user names only one stage.
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