Data for World Models & Autonomous Intelligence
Move beyond generative AI. We provide the high-dimensional sensory data required to train World Models—architectures that learn to represent, predict, and reason about the physical world in latent space.
01 // JEPA Training Data
Joint-Embedding Predictive Architecture (JEPA) requires datasets that avoid pixel-level reconstruction. We curate multi-modal video sequences specifically for latent-space energy-based learning.
- Non-Generative Masks
- Abstracted Visuals
- Energy-Based Labels
02 // Temporal Prediction
World Models must predict the consequences of actions over time. We provide high-frame-rate causal video datasets with synchronized IMU and sensory telemetry.
- Causal Action Pairs
- Multi-View Sync
- Physical Meta-Data
03 // Physical Grounding
Datasets designed for learning gravity, friction, and object permanence. We bridge the Sim2Real gap with precise real-world robotic interaction data.
- Friction/Force Logs
- Occlusion Sequences
- Robotic Telemetry
Simulation-to-Reality (Sim2Real)
We provide the sensory bridge for World Models to transition from simulated environments to unpredictable real-world physics.
PREDICTION
Future State Estimation
Paired video-action sequences for training predictive encoders in autonomous driving and flight.
SENSORY
Multi-Modal Fusion
Aligned LiDAR, Radar, Thermal, and Acoustic data for holistic world state representation.
LOGIC
Object Permanence
Datasets specifically curated to train models on reasoning about hidden or occluded objects.
INTERACTION
Kinesthetic Data
Real-world robotic arm sensory logs for training internal models of physical interaction.
Engineer Your World Model
Connect with our specialists to discuss custom JEPA-aligned datasets or high-fidelity sensory collection.