Dual-Gripper Tactile Pressure Data
Dexterous finger pressure arrays capturing object slip detection, grasp force distributions, and micro-texture contact patterns.
Quality & Precision Benchmarks
PRESSURE SENSING RANGE
0 to 50 kPa
SPATIAL CELL PITCH
1.2 mm
RESPONSE LATENCY
< 1.5 ms
GRASP PRECISION
99.6%
Dataset Taxonomy & Output Structure
tactile_grid_left (16x16 Piezoresistive Matrix)
tactile_grid_right (16x16 Piezoresistive Matrix)
slip_probability_score (0.0 to 1.0)
surface_friction_coeff (Mu Rating)
Specific Type Tasks & Applications
- • Fruit & Food Sorting
- • Deformable Cloth & Cable Manipulation
- • High-Friction Surface Grasping
- • Dynamic Object Slip Detection
What Is Right vs What Is Wrong
| COMMON COMPETITOR ERRORS (WRONG) | BLUE PROJECTS GROUND TRUTH (RIGHT) |
|---|---|
| FAIL: Binary open/close gripper state logging without pressure arrays | PASS: Multi-point tactile pressure grid mapping across fingertips |
| FAIL: Unlabeled object deformation responses | PASS: Synchronized video + tactile pressure arrays during grasping |
Files & Telemetry Data Example (Python `h5py`)
import h5py
import numpy as np
# Load Blue Projects Type Dataset: Dual-Gripper Tactile Pressure Data
with h5py.File('dual-gripper-tactile-data_episode_001.h5', 'r') as f:
joint_data = np.array(f['observations/qpos'])
print("Loaded joint data shape:", joint_data.shape)
Why Blue Projects for Dual-Gripper Tactile Pressure Data?
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