Ndolphin/SoftManipulator_sim2real

SoftManipulator Sim2Real Dataset

This dataset accompanies the research paper "Bridging High-Fidelity Simulations and Physics-Based Learning Using A Surrogate Model for Soft Robot Control" published in Advanced Intelligent Systems, 2025.

📋 Dataset Overview

This dataset contains experimental and simulation data for a 3-actuator pneumatic soft manipulator, designed to enable sim-to-real transfer learning and surrogate model development. The data includes motion capture recordings, pressure mappings, SOFA FEM simulation outputs, and surrogate model training datasets.

🎯 Dataset Purpose

• —Sim2Real Research: Bridge the gap between SOFA simulations and real hardware
• —Surrogate Model Training: Train neural networks for fast dynamics prediction
• —Model Calibration: Calibrate FEM parameters using real-world data
• —Workspace Analysis: Understand the robot's range of motion and capabilities
• —Validation: Compare simulation outputs with experimental ground truth

📊 Dataset Files

🔧 Data Collection Setup

Hardware Configuration

• —Robot: 3-cavity pneumatic soft manipulator (silicone, ~150mm length)
• —Actuation: Pneumatic pressure control (-20 kPa to +35 kPa per cavity)
• —Sensing: 6-DOF motion capture system (OptiTrack), pressure sensors
• —Materials: Ecoflex 00-30 silicone with embedded pneumatic chambers

Simulation Environment

• —SOFA Framework: v22.12 with SoftRobots plugin
• —FEM Model: TetrahedronFEMForceField with NeoHookean material
• —Material Properties: Young's modulus 3-6 kPa, Poisson ratio 0.41
• —PyBullet: v3.2.5 for surrogate model validation

📈 Data Schema

Joint Space Data

• —thetaX, thetaY: Joint angles (radians, -π/4 to π/4)
• —d: Linear displacement (mm, 0 to 50)

Pressure Commands

• —P1, P2, P3: Cavity pressures (Pa, -20000 to 35000)

Cartesian Space

• —TCP_X, TCP_Y, TCP_Z: Tool center point position (mm)
• —Normal_X, Normal_Y, Normal_Z: End-effector orientation

Forces

• —Fx, Fy, Fz: External forces (N, contact/manipulation tasks)

Temporal Information

• —Time: Timestamp (seconds)
• —Episode: Experiment episode number

🚀 Usage Examples

Loading Data in Python

import pandas as pd
from datasets import load_dataset

# Load from HuggingFace
dataset = load_dataset("Ndolphin/SoftManipulator_sim2real")

# Or load locally
df = pd.read_csv("ForwardDynamics_Pybullet_joint_to_pos.csv")
print(f"Dataset shape: {df.shape}")
print(f"Columns: {df.columns.tolist()}")

Training a Surrogate Model

# Pressure to joint angle mapping
X = df[['P1', 'P2', 'P3']].values  # Pressure inputs
y = df[['thetaX', 'thetaY', 'd']].values  # Joint outputs

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Train your neural network model

Motion Analysis

# Analyze workspace coverage
import matplotlib.pyplot as plt

tcp_data = df[['TCP_X', 'TCP_Y', 'TCP_Z']]
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(tcp_data['TCP_X'], tcp_data['TCP_Y'], tcp_data['TCP_Z'])
ax.set_title('Robot Workspace')

📝 Data Quality & Preprocessing

Quality Assurance

• —Filtering: Outliers removed using 3-sigma rule
• —Smoothing: Savitzky-Golay filter applied to motion capture data
• —Synchronization: All sensors synchronized to 100Hz sampling rate
• —Validation: Cross-validated against multiple experimental runs

Recommended Preprocessing

from sklearn.preprocessing import StandardScaler

# Normalize features for neural network training
scaler = StandardScaler()
X_normalized = scaler.fit_transform(X)

# Save scaler for inference
import joblib
joblib.dump(scaler, 'scaler.pkl')

🎓 Citation

If you use this dataset in your research, please cite:

@article{hong2025bridging,
  title={Bridging High-Fidelity Simulations and Physics-Based Learning Using A Surrogate Model for Soft Robot Control},
  author={Hong, T. and Lee, J. and Song, B.-H. and Park, Y.-L.},
  journal={Advanced Intelligent Systems},
  year={2025},
  publisher={Wiley}
}

📄 License

This dataset is released under the MIT License. See LICENSE file for details.

🤝 Contact

For questions about the dataset or research:

• —Authors: T. Hong, J. Lee, B.-H. Song, Y.-L. Park
• —Institution: [Your Institution]
• —Email: [Contact Email]
• —Paper: [ArXiv/DOI Link when available]

🔍 Related Resources

• —Code Repository: https://github.com/ndolphin-github/Sim2RealframeworkSoftRobot
• —SOFA Simulations: Included in the repository
• —Pre-trained Models: Available in the code repository
• —Demo Videos: SOFA simulation demos included

Dataset Version: 1.0 | Last Updated: October 2025