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4.7948560e-001 4.0996980e-001 1.7279370e-001 -1.7946250e-002 -1.5951180e-001 -2.9333700e-001 -3.4621050e-001 -4.0094000e-001 -4.3137480e-001 -4.1270170e-001 -4.1287520e-001 -3.5437050e-001 -2.2045400e-001 -8.4550830e-002 -1.7426470e-002 1.1427780e-001 3.7547380e-001 4.9134260e-001 4.4457660e-001 4.1415510e-001... |
-3.2747630e-001 -2.2285050e-001 -1.6739390e-001 -1.1461470e-001 4.9832610e-002 3.0675680e-001 4.6571720e-001 3.6668160e-001 2.7110590e-001 3.6079430e-001 4.2491010e-001 4.1065760e-001 1.7089910e-001 -2.5246440e-001 -2.4589530e-001 5.7464210e-002 1.1401380e-001 1.6551680e-002 -1.4113960e-001 -2.3251920e-001... |
3.9096120e-001 2.2725430e-001 -9.0405060e-002 -2.4504400e-001 -1.5015080e-001 1.1784780e-001 1.5628520e-001 -4.7276380e-002 -1.9106300e-001 -3.1753200e-001 -3.3691820e-001 -3.2801840e-001 -2.4957890e-001 1.1307210e-002 4.9899410e-002 -6.2778510e-002 -1.2497040e-001 -1.6341420e-001 -6.9506950e-002 -2.8867950e-003... |
-1.8837790e-001 -2.5939840e-001 -2.9494190e-001 -2.0732350e-001 -1.0851790e-001 -2.5815900e-002 -4.4634110e-002 -1.2419210e-001 -1.2806600e-001 -1.0458510e-001 -2.4563590e-002 4.7912160e-002 4.5138600e-002 7.2709470e-002 8.6947040e-002 8.6969220e-002 1.5224040e-001 2.4119970e-001 2.4652470e-001 9.5923750e-002... |
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🤖 Robot Intelligence Dataset
A collection of three real-world robotics sensor datasets used to train and evaluate machine learning pipelines across four intelligence challenges: Perception · Navigation · Failure Detection · Autonomous Decision-Making (RL).
Companion GitHub repo: KushSaraf/Robot_Intelligence
Made by: Kush Saraf & Yash Chavda
Datasets
🧠 1. Perception Dataset — Human Activity Recognition (HAR)
Source: UCI HAR Dataset
| Property | Value |
|---|---|
| Instances | 10,299 (7,352 train / 2,947 test) |
| Features | 561 time & frequency domain features |
| Sensors | Accelerometer + Gyroscope (3-axis, 50 Hz) |
| Classes | 6 (Walking, Walking Upstairs, Walking Downstairs, Sitting, Standing, Laying) |
| Device | Samsung Galaxy S II worn on waist |
| Subjects | 30 volunteers, ages 19–48 |
Task: Classify human activity from smartphone inertial sensor signals.
Files:
perception_dataset/
├── train/X_train.txt # 7352 × 561 feature matrix
├── train/y_train.txt # labels 1–6
├── train/subject_train.txt # subject IDs
├── test/X_test.txt
├── test/y_test.txt
├── test/subject_test.txt
├── features.txt # feature names
├── activity_labels.txt # class names
└── train/Inertial Signals/ # raw inertial signals (9 channels)
🗺️ 2. Navigation Dataset — Wall-Following Robot
Source: UCI Wall-Following Robot Navigation
| Property | Value |
|---|---|
| Instances | 5,456 |
| Features | 24 ultrasonic range sensors (US1–US24, 360°) |
| Classes | 4 movement decisions |
| Robot | SCITOS G5 |
| Sampling | ~9 samples/second |
Class distribution:
| Class | Count | % |
|---|---|---|
| Move-Forward | ~2,205 | 40% |
| Sharp-Right-Turn | ~2,074 | 38% |
| Slight-Right-Turn | ~818 | 15% |
| Slight-Left-Turn | ~359 | 6% |
Task: Sensor array → movement command (non-linearly separable by design).
Files:
navigation_dataset/
├── sensor_readings_24.data # 24 sensors (recommended)
├── sensor_readings_4.data # 4 simplified sensors
├── sensor_readings_2.data # 2 simplified sensors
└── Wall-following.names # dataset description
⚠️ 3. Failure Detection Dataset — Robot Execution Failures
Source: UCI Robot Execution Failures
| Property | Value |
|---|---|
| Instances | ~463 total across 5 tasks |
| Raw features | 90 (15 time-steps × 6 force/torque axes) |
| Stat features | 24 (mean, std, min, max per axis) |
| Total features | 114 (after feature engineering) |
| Classes | Up to 11 per file |
Force/torque axes: Fx, Fy, Fz, Tx, Ty, Tz
Learning problems (files):
| File | Task |
|---|---|
| lp1.data | Approach to grasp position |
| lp2.data | Transfer of a part |
| lp3.data | Position after transfer failure |
| lp4.data | Approach to ungrasp position |
| lp5.data | Motion with a part |
Task: Classify robot arm execution failures from time-series force/torque data.
Files:
failure_dataset/
├── lp1.data # approach to grasp
├── lp2.data # transfer
├── lp3.data # post-transfer
├── lp4.data # ungrasp approach
└── lp5.data # motion with part
Usage
Perception
import pandas as pd
X_train = pd.read_csv("perception_dataset/train/X_train.txt", sep=r"\s+", header=None)
y_train = pd.read_csv("perception_dataset/train/y_train.txt", header=None, names=["label"])
Navigation
import pandas as pd
df = pd.read_csv("navigation_dataset/sensor_readings_24.data", header=None)
X, y = df.iloc[:, :-1], df.iloc[:, -1]
Failure Detection
# Blocks are separated by blank lines; each block = one instance
# Format: label\n6_values\n6_values\n...\n (15 timesteps)
def parse_lp(path):
rows = []
with open(path) as f:
text = f.read()
for block in text.strip().split("\n\n"):
lines = block.strip().split("\n")
label = lines[0].strip()
data = [list(map(float, l.split())) for l in lines[1:] if l.strip()]
rows.append({"label": label, "data": data})
return rows
Citation
If you use this dataset collection, please cite the original UCI sources:
- HAR: Anguita et al., ESANN 2013
- Wall-Following: Freire et al., ESANN 2009
- Robot Failures: Camarinha-Matos et al., 1996
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