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Isaac GR00T-Dreams for Synthetic Trajectory Data Generation

Authors: NVIDIA Isaac Team

Organization: NVIDIA

Overview

Model Workload Use Case
Cosmos Predict 2 Post-training Synthetic trajectory data generation for humanoid robots

Isaac GR00T-Dreams leverages Cosmos Predict 2 to generate synthetic trajectory data for teaching humanoid robots new actions in novel environments. By using world foundation models, a small team can create training data that would otherwise require thousands of demonstrators.

Key Features

  • Scalable Generation: Produce large-scale synthetic trajectories from minimal human demonstrations
  • Environment Generalization: Adapt to new environments without extensive retraining
  • Diverse Behaviors: Cover wide-ranging scenarios and edge cases
  • Cost-Effective: Dramatically reduce manual data collection effort

How It Works

  1. Start with Demonstrations: Use a small set of human demonstration videos
  2. Generate Variations: Apply Cosmos Predict 2 to create synthetic trajectories with environmental variations
  3. Scale Training Data: Produce thousands of variations from each demonstration
  4. Train Policies: Use synthetic data to train robust robot control policies

Applications

  • Humanoid locomotion (walking, running, navigation)
  • Object manipulation and interaction
  • Multi-terrain adaptation
  • Rare scenario and edge case coverage

Resources