Force-Torque Haptic Feedback Data
Multi-axis load cell telemetry capturing object contact dynamics, surface insertion friction, and mechanical collision impulse profiles.
Quality & Precision Benchmarks
FORCE RESOLUTION
0.01 N
TORQUE SENSING ACCURACY
0.005 N*m
SAMPLING FREQUENCY
100Hz Hardware Latch
IMPEDANCE CONTROL ACCURACY
99.4%
Dataset Taxonomy & Output Structure
fx, fy, fz (3-Axis Linear Force in N)
tx, ty, tz (3-Axis Rotational Torque in N*m)
contact_impulse_flag (Boolean Latch)
stiffness_matrix_estimate (6x6 Matrix)
Specific Type Tasks & Applications
- • Peg-in-Hole Precision Insertion
- • Fragile Glass & Deformable Object Handling
- • Industrial Bolt Fastening & Thread Alignment
- • Collision Impulse Guardrail Tuning
What Is Right vs What Is Wrong
| COMMON COMPETITOR ERRORS (WRONG) | BLUE PROJECTS GROUND TRUTH (RIGHT) |
|---|---|
| FAIL: Uncalibrated load cells missing contact timestamps | PASS: 6-Axis force-torque load cell logging synchronized to joint state at 100Hz |
| FAIL: Ignoring object collision impulses during contact phases | PASS: Full contact impulse profile recording with stiffness matrix estimation |
Files & Telemetry Data Example (Python `h5py`)
import h5py
import numpy as np
# Load Blue Projects Type Dataset: Force-Torque Haptic Feedback Data
with h5py.File('force-torque-haptic-telemetry_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 Force-Torque Haptic Feedback Data?
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