🤖 Cross‑Domain Transfer Learning: From Human Motion to Robot Fault Detection A machine learning pipeline that leverages human motion data to detect robot joint faults using LSTM‑based residual models and transfer learning.

🧠 Project Overview This project trains a Long Short‑Term Memory (LSTM) residual model on human motion sequences, then transfers that learned representation to detect faults in robotic joints. By mapping robot sensor features into a human‑motion–like feature space and fine‑tuning selectively, the system adapts to robot fault detection tasks effectively. arxiv.org +4 github.com +4 github.com +4

🚀 Key Features LSTM Residual Network to capture temporal structure in motion/fault data.

Transfer Learning Pipeline:

Train on human motion data.

Map robot features into the learned human-motion feature space.

Freeze early layers of the network for fine-tuning on robot data.

Training Enhancements: Class weighting, callbacks, and feature‑importance evaluation to improve model robustness and interpretability. kpertsch.github.io +4 github.com +4 github.com +4

📂 Repository Contents robot_fault_detection.ipynb: Jupyter notebook for data processing, model training, fine-tuning, and evaluation.

best_robot_model.h5: Pretrained/final model weights (optional).

dataset/: Human motion and robot sensor data (if included).

requirements.txt: Python dependencies.

README.md: Project documentation.

🛠️ Setup Instructions

1. Clone the Repository git clone https://github.com/Subhash-777/Cross-Domain-transfer-learning-from-Human-Motion-to-Robot-Fault-Detection.git cd Cross-Domain-transfer-learning-from-Human-Motion-to-Robot-Fault-Detection
2. Install Dependencies pip install -r requirements.txt
3. Prepare the Data Ensure your human motion data and robot sensor logs are formatted appropriately:

Human motion sequences for training.

Robot joint data for fine‑tuning. Follow the patterns in the example notebook for preprocessing and feature alignment.

4. Train the Model Use the Jupyter notebook or command‑line scripts to train the LSTM model on human data:

5. Transfer & Fine‑Tune for Fault Detection Map robot data into the human-motion feature space, freeze early layers, and fine‑tune on robot dataset:

6. Evaluate & Analyze Evaluate model performance and analyze feature importance:

🔍 How It Works Preprocessing: Clean and format sequential data from both domains.

Feature Adaptation: Transform robot sensor data to align with the human-motion embedding space.

Residual-LSTM Model: Capture temporal dynamics while leveraging residual connections.

Transfer Learning: Freeze lower-level weights and fine-tune upper layers on robot fault detection.

Training Enhancements: Use callbacks, class weights, and visualization tools for better training and diagnostics.

📦 Technology Stack Python 3.x

TensorFlow / Keras (LSTM networks)

NumPy, Pandas: Data manipulation

Matplotlib / Seaborn: Visualization

Jupyter Notebook: Interactive walkthroughs

🧪 Results & Metrics The model was evaluated using:

Fault detection accuracy on robot joint data.

Class-weighted loss to balance class imbalance.

Feature importance analysis to interpret which features contributed most to the detection.

(Include specific metrics, training curves, and confusion matrices in this section as needed.)

🤝 Contributing Contributions welcome! To contribute:

Fork the repo.

Create a new branch (e.g. feature-improvement).

Commit your changes.

Submit a pull request with clear descriptions and documentation.

📚 References The method follows research in cross-domain transfer learning and semantic imitation. For similar work on leveraging human demonstrations to train robots, see Cross‑Domain Transfer via Semantic Skill Imitation. github.com +2 github.com +2 github.com +2 github.com +1 github.com +1 softwarepatternslexicon.com +3 github.com +3 github.com +3 kpertsch.github.io +2 pure.kaist.ac.kr +2 arxiv.org +2