Transform human intelligence into
Robot Intelligence
Build a universal robot foundation model system based on large-scale neural data and multimodal learning
VLA model
End-to-end model with Vision-Language-Action inputs mapping to action outputs, enabling zero-shot generalization and cross-task transfer learning.
Vision
Process multimodal visual inputs such as RGB images, depth maps, and point clouds to understand scene geometry and object attributes.
Language
Parse natural language instructions to understand task objectives, constraints, and context.
Action Generation
Output robot control signals such as joint angles, end-effector pose, and force control parameters.
World Model
Build an internal world model to enable robots to imagine and predict action outcomes, supporting more efficient learning and planning.
Environment Simulation
Build a virtual training environment that supports domain randomization and scenario generation.
Action Result Prediction
Predict state changes and outcomes after executing the given action
Haptic Feedback
Simulate real-world physics characteristics and contact dynamics
Counterfactual reasoning
Simulate outcomes of different actions to support planning and optimization
Data Augmentation Capabilities
Efficiently build large-scale, high-quality training datasets
Synthetic Data Generation
Automatically generate large-scale annotated data in virtual environments
- Procedural Scene Generation
- Automated Annotation System
- Domain randomization augmentation
Data Augmentation
Transform and expand based on existing data
- Viewpoint Change
- Lighting changes
- Object Replacement
- Background Compositing
Auto-label
Semi-automatic labeling using pre-trained models
- Key Point Detection
- Semantic segmentation
- Pose Estimation
- Quality Assessment
Behavior Prediction
Predict future behavior patterns based on historical data
- Trajectory Prediction
- Intent Recognition
- Anomaly Detection
Learn about model services
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