Machine tending
Generate simulation-ready bin picking scenes for machine tending — identical parts in random orientations, physically accurate materials, and multi-modal training outputs for pick-and-place robots.
From bin scene to training data
Control part geometry, bin density, lighting, and camera setup in Isaac Sim — then export aligned RGB, normal, and depth modalities from the same scene for VLA and manipulation model training.
Pipeline
Random bin poses
Generate densely packed or sparse arrangements of identical parts with arbitrary orientations — the core feed-bin scenario for machine tending pick-and-place.
Controlled variation
Vary part count, lighting direction, container geometry, and camera height while keeping physics-grounded assets and scene parameters consistent.
Geometry-aware renders
Export normal maps and depth from the same scene so perception models learn true part geometry, not just appearance under one lighting setup.
Training-ready labels
Produce RGB, depth, segmentation, bounding boxes, and COCO-format annotations ready for VLA training and robot deployment.
Build the Future of Physical AI Systems
Accelerate the journey from synthetic worlds to real-world deployment with a platform designed for continuous learning, adaptation, and scale.
