From CAD files to trained policies.

Trained in sim, proven on hardware. An RL locomotion policy trained from scratch in Isaac Lab, zero-shot deployed to a real Unitree Go2.

VLA and world-foundation models - GR00T post-training driving real manipulation, plus NVIDIA Cosmos world & action generation, benchmarked on DGX Spark.

Whole-body control at scale. Massively parallel RL locomotion for humanoid robots in Isaac Lab - one pipeline, any embodiment, on Blackwell-class GPUs.

Thousands of robots, one policy. Massively parallel RL / IL on UR5e, Franka, KUKA and ABB - on Blackwell-class GPUs.

High-accuracy socket and plug geometry with realistic contact constraints - fine alignment, insertion-force modeling and collision-aware cable deformation during autonomous plug-into-socket insertion.

Teleoperation and imitation learning with Isaac Lab via VR control - plus the Digit humanoid VR data-collection rig. Human demos in, robot skills out, scaled 100-1000× with Isaac Lab Mimic.

A few demos become thousands. Isaac Lab Mimic multiplies human demonstrations into large, diverse training datasets - the fuel for VLA and policy post-training, on Blackwell RTX 6000 Pro.

The hard physics others avoid - cloth, fluids and contact-rich dynamics, plus weather effects that keep LiDAR and cameras honest.

Commission the line in sim first - conveyors, sorters and forklift / MHE fleets with synchronized actuators.