Leader-Follower Arm Telemetry Rigs
50Hz joint angle and velocity trajectory logging using twin 7-DOF master-slave arm setups for imitation learning and VLA policy training.
Quality & Precision Benchmarks
ENCODER KINEMATIC DRIFT
< 0.04° / sec
TEMPORAL SYNC JITTER
< 0.2 ms
ANNOTATION CONSENSUS
99.8%
SAMPLING FREQUENCY
50Hz (20ms Ts)
Dataset Taxonomy & Output Structure
qpos_0..13 (Joint Angles in Radians)
qvel_0..13 (Joint Angular Velocities in rad/s)
efforts_0..13 (Torque Vectors in N*m)
gripper_aperture_0..1 (Normalized Metric)
Specific Type Tasks & Applications
- • Dual-Arm Manipulator Calibration
- • Trajectory Smoothness Optimization
- • Imitation Learning Policy Fine-Tuning
- • Joint Limits & Singularity Avoidance
What Is Right vs What Is Wrong
| COMMON COMPETITOR ERRORS (WRONG) | BLUE PROJECTS GROUND TRUTH (RIGHT) |
|---|---|
| FAIL: Single-arm pick trajectory without kinematic synchronization | PASS: 50Hz synchronized dual-arm joint angles, velocities, and torque vectors |
| FAIL: Joint encoder drift exceeding 2.0 degrees over 100s episodes | PASS: Sub-millisecond optical encoder calibration with zero accumulated drift (< 0.04°) |
Files & Telemetry Data Example (Python `h5py`)
import h5py
import numpy as np
# Load Blue Projects Type Dataset: Leader-Follower Arm Telemetry Rigs
with h5py.File('leader-follower-arm-rigs_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 Leader-Follower Arm Telemetry Rigs?
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