OliverUrbann/HumanoidRobotSoccer

Fall Prediction Dataset for Humanoid Robots

Dataset Summary

This dataset consists of 37.9 hours of real-world sensor data collected from 20 Nao humanoid robots over the course of one year in various test environments, including RoboCup soccer matches. The dataset includes 18.3 hours of walking data, featuring 2519 falls. It captures a wide range of activities such as omni-directional walking, collisions, standing up, and falls on various surfaces like artificial turf and carpets.

The dataset is primarily designed to support the development and evaluation of fall prediction algorithms for humanoid robots. It includes data from multiple sensors, such as gyroscopes, accelerometers, and force-sensing resistors (FSR), recorded at a high frequency to track robot movements and falls with precision.

Using this dataset, the RePro-TCN model was developed, which outperforms existing fall prediction methods under real-world conditions. This model leverages temporal convolutional networks (TCNs) and incorporates advanced training techniques like progressive forecasting and relaxed loss formulations.

Dataset Structure

• Duration: 37.9 hours total, 18.3 hours of walking
• Falls: 2519 falls during walking scenarios
• Data Types: Gyroscope (roll, pitch), accelerometer (x, y, z), body angle, and force-sensing resistors (FSR) per foot.

Use Cases

• Humanoid robot fall prediction and prevention
• Robot control algorithm benchmarking
• Temporal sequence modeling in robotics

Licensing

This dataset is shared under the apache-2.0 license, allowing use and modification with proper attribution, as long as derivatives are shared alike.

Citation

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

How to Use the Dataset

To get started with the Fall Prediction Dataset for Humanoid Robots, follow the steps below:

1. Set Up a Virtual Environment

It's recommended to create a virtual environment to isolate dependencies. You can do this with the following command:

python -m venv .venv

After creating the virtual environment, activate it:

• On Windows:

  .venv\Scripts\activate
  

• On macOS/Linux:

  source .venv/bin/activate
  

2. Install Dependencies

Once the virtual environment is active, install the necessary packages by running:

pip install -r requirements.txt

3. Run the Example Script

To load and use the dataset for training a simple LSTM model, run the usage_example.py script:

python usage_example.py

This script demonstrates how to:

• Load the dataset
• Select the relevant sensor columns
• Split the data into training and test sets
• Train a basic LSTM model to predict falls
• Evaluate the model on the test set

Make sure to check the script and adjust the dataset paths if necessary. For further details, see the comments within the script.

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license: apache-2.0