Physical AI Grippers are essential for enabling robots to grasp, manipulate, and interact with objects in real-world environments. At Nferent AI, we provide advanced grippers and end effectors for robotics data collection, embodied AI training, and intelligent automation. Our ecosystem includes 2-finger parallel grippers, 3-finger adaptive grippers, anthropomorphic robotic hands, vacuum grippers, and soft grippers that capture grip force, finger articulation, contact points, object pose, and slip detection data. These datasets help train Vision Language Action (VLA) models, support humanoid data collection, improve world models, and accelerate sim-to-real learning for next-generation Physical AI systems.
Hardware Types
Common types and variants within this category
2-Finger Parallel Gripper
3-Finger Adaptive Gripper
4-Finger Anthropomorphic Gripper
5-Finger Dexterous Robotic Hand
Data Captured
Types of data collected using this hardware
Grip force
Finger articulation
Contact point
Object pose
Slip detection
Tactile feedback
Grasp success/failure
Contact pressure
Typical Parameters
Key parameters captured in datasets
Finger DOF
Force feedback (N)
Slip detection sensitivity
Grasp success rate (%)
Contact pressure (Pa)
Finger speed (m/s)
Payload capacity (kg)
Response time (ms)
Dataset Capture Example
Capture diverse grasping strategies with force feedback and tactile sensing across various objects.
Key Features:
Synchronized sensors
High-quality calibration
Real-world scenarios
Complete metadata
Example Use Cases
Industries and applications using this hardware
Dexterous manipulation
Industrial picking
Humanoid robotics
Bin picking
Object sorting
Precision assembly
Featured Hardware
Hardware Options
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