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README.md

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+# 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
+
+| File | Size | Samples | Description | Usage |
+|------|------|---------|-------------|--------|
+| `ForwardDynamics_Pybullet_joint_to_pos.csv` | ~66MB | 100,000+ | PyBullet forward dynamics: joint commands → TCP positions | Surrogate model training |
+| `MotionCaptureData_ROM.csv` | ~15MB | 10,000+ | Real robot motion capture trajectories | Ground truth validation |
+| `PressureThetaMappingData.csv` | ~2MB | 5,000+ | Pressure inputs → joint angle outputs | Actuation mapping |
+| `Pressure_vs_TCP.csv` | ~8MB | 8,000+ | Pressure commands → tool center point positions | Control modeling |
+| `RealPressure_vs_SOFAPressure.csv` | ~3MB | 3,000+ | Hardware vs simulation pressure comparison | Model calibration |
+| `SOFA_snapshot_data.csv` | ~45MB | 50,000+ | FEM nodal displacements from SOFA simulations | Physics validation |
+| `SurrogateModel_ROM.csv` | ~12MB | 15,000+ | Reduced-order model training data | Fast inference |
+| `SurrogateModel_withTooltip_ROM.csv` | ~18MB | 20,000+ | ROM data with tooltip contact forces | Contact modeling |
+
+## 🔧 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
+```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
+```python
+# 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
+```python
+# 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
+```python
+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:
+
+```bibtex
+@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/Sim2Real_framework_SoftRobot
+- **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*