README.md

metadata

language:
  - en
tags:
  - robotics
  - soft-robotics
  - sim2real
  - physics-simulation
  - neural-networks
  - pneumatic-actuation
  - motion-capture
  - surrogate-modeling
  - fem-simulation
  - sofa-framework
size_categories:
  - 100K<n<1M
task_categories:
  - tabular-regression
  - time-series-forecasting
task_ids:
  - tabular-single-column-regression
  - univariate-time-series-forecasting
pretty_name: Soft Manipulator Sim2Real Dataset
configs:
  - config_name: default
    data_files:
      - '*.csv'
dataset_info:
  features:
    - name: P1
      dtype: float64
      description: Pneumatic pressure for cavity 1 (Pa)
    - name: P2
      dtype: float64
      description: Pneumatic pressure for cavity 2 (Pa)
    - name: P3
      dtype: float64
      description: Pneumatic pressure for cavity 3 (Pa)
    - name: thetaX
      dtype: float64
      description: Joint angle X-axis (radians)
    - name: thetaY
      dtype: float64
      description: Joint angle Y-axis (radians)
    - name: d
      dtype: float64
      description: Linear displacement (mm)
    - name: TCP_X
      dtype: float64
      description: Tool center point X position (mm)
    - name: TCP_Y
      dtype: float64
      description: Tool center point Y position (mm)
    - name: TCP_Z
      dtype: float64
      description: Tool center point Z position (mm)
  splits:
    - name: train
      num_bytes: 167000000
      num_examples: 200000
  download_size: 167000000
  dataset_size: 167000000
license: mit
paperswithcode_id: null

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

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/Sim2Real_framework_SoftRobot
• SOFA Simulations: Included in the repository
• Pre-trained Models: Available in the code repository
• Demo Videos: SOFA simulation demos included

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Dataset Version: 1.0 | Last Updated: October 2025