From Human Intelligence to Robot Intelligence
Data Infrastructure
A complete end-to-end technical pipeline that converts human action intent into data strategies for robot learning.
Human Intelligence
Human intent, force generation logic, and muscle coordination patterns during action execution
By observing and analyzing natural human movements, understand the underlying reasons and goals—not just the surface trajectories.
Neural wristband data collection
Non-invasive dual-mode signal acquisition device
8-channel neural electrophysiology sensor + EMG sensor, 2KHz sampling rate for precise capture of millisecond-level neural signal changes.
Neurophysiological Data
EMG muscle signals + neural electrical signals
Simultaneously record muscle activity and nerve impulses to fully reconstruct the body's force generation process and movement control mechanisms.
AI Data Processing Platform
Data cleaning, labeling, augmentation, and quality assessment
Automated data preprocessing pipeline for efficient processing and quality management of large-scale datasets.
VLA model
Vision-Language-Action Multimodal Fusion
Unify visual perception, language understanding, and action execution into a single end-to-end model to enable zero-shot generalization.
World Model
Environment simulation, physical feedback, result prediction
Build a virtual environment simulator to predict action outcomes, provide physical feedback, and accelerate policy learning and validation.
Robot Strategy Model
Action generation, force control strategy, adaptive adjustment
Generate robot motion policies and force control parameters tailored to different scenarios based on learned data distributions.
Embodied AI Applications
Humanoid robots, dexterous hands, industrial robots
Deploy and validate on real-world robot platforms to achieve human-level dexterity and adaptability.
Core Technology Modules
Core capabilities powering the entire tech stack
Low-latency action generation
Millisecond-level response speed for real-time human-machine interaction.
- <10ms end-to-end latency
- High-frequency control loop
- Real-time haptic feedback
Model Lightweighting
Optimized for edge deployment; runs on embedded devices
- Model Compression and Distillation
- Quantization Acceleration
- Edge Inference Optimization
Simulation Data Augmentation
Generate large-scale training data in virtual environments
- Physics Engine Integration
- Domain Randomization
- Auto-label
Behavior Understanding Model
Understand the intent and cause-and-effect behind an action
- Intent Recognition
- Causal Inference
- Context Modeling
View technical details
Connect with our technical team for detailed documentation and whitepapers.