Robot Learning
Synthetic data generation, multi-paradigm policy training, and sim-to-real deployment powered by NVIDIA Cosmos.
Robot Learning combines NVIDIA Cosmos world foundation models with multiple training paradigms to develop robust robot policies. Generate synthetic training data with Cosmos Predict, train through imitation learning or RL, and bridge the sim-to-real gap with Cosmos Transfer for deployment on physical hardware.
Multi-paradigm policy training with simulation validation.
What's Included
Synthetic Data Generation
Use Cosmos Predict to generate physically plausible future-state video for training data augmentation without expensive real-world data collection.
Imitation Learning
Train policies from human demonstrations. Efficient for manipulation tasks with clear success criteria and available expert data.
Reinforcement Learning
Multi-agent RL environments for navigation, exploration, and complex sequential decision-making with sparse rewards.
Supervised & Self-Supervised
Classical supervised learning for perception and self-supervised pre-training for visual representation learning.
Sim-to-Real Transfer
Cosmos Transfer bridges simulation and reality through video-to-video domain translation, closing the visual domain gap.
Specs & Parameters
Use Cases
Warehouse AMR Navigation
Train autonomous mobile robots to navigate dynamic warehouse environments with obstacle avoidance and path planning.
Industrial Manipulation
Robotic arm policies for pick-and-place, assembly, and inspection tasks with force-feedback control.
Tactical Autonomous Systems
Train robots for GPS-denied navigation, multi-agent coordination, and contested environment operations.
Ready for Robot Learning?
Typical engagement: 4-8 weeks. From assessment to deployment, Cortex handles the full pipeline.