Solutions · 01 · Embodied AI & Humanoid Robotics
The demonstrations are the bottleneck. Not the GPUs.
Teams building general-purpose robot brains — end-to-end vision-language-action models that have to transfer across humanoids, arms and mobile bases.
The problem
Real demonstrations are the scarcest resource in robotics. A teleoperation session produces a few hundred episodes; a policy that generalises wants orders of magnitude more — across lighting, object poses, materials and failure cases no operator would think to stage. The largest open real-robot dataset ever assembled, pooled from 34 labs, holds roughly a million trajectories. Most teams have a fraction of a percent of that, and collecting more means a human in a room with a robot, one episode at a time.
How the loop runs here
Your demonstrations go in; domain-randomised variations come out. World foundation models expand each episode across lighting, backgrounds, object geometry and physical properties, and an evaluator scores which generated data is fit to train on.
Pre-train a VLA model from scratch, or fine-tune an open robot foundation model such as NVIDIA Isaac GR00T on your own embodiment. Reinforcement and imitation learning run across thousands of simulated environments in parallel.
Dexterous manipulation trials in simulation before a single physical trial — grasp success, contact forces, recovery from disturbance — so the robot lab tests what already works, not what might.
What we provide
- Generation capacity that does not compete with your training jobs for GPUs
- Multi-node clusters for foundation-model pre-training, reserved by contract
- Parallel simulation for reinforcement and imitation learning at scale
- Storage sized for episode libraries that grow every week
Industry context
Open X-Embodiment pooled 60 datasets from 34 robotics labs into over 1 million real robot trajectories across 22 embodiments — still small next to the text corpora behind today's language models.
Source: Open X-Embodiment (RT-X)
NVIDIA Isaac Lab sustains 4,096 parallel environments at over 135,000 frames per second on humanoid locomotion, turning policy training from days into minutes.
Source: NVIDIA Technical Blog