Project Background:
Robotics and automation revolve around deploying machine learning models in distributed robotic systems while respecting data privacy and minimizing communication overhead. Traditional robotics and automation approaches often rely on centralized data processing and model training, which may not be feasible or efficient in decentralized environments. Federated learning offers a promising solution by enabling model training directly on robotic devices or edge computing nodes without sharing raw data with a central server. This approach allows individual robots or robotic systems to learn from their local data while preserving privacy and confidentiality. In robotics and automation, federated learning enables collaborative model training across a network of robots or automation devices, capturing the environmental variations in different locations.
Additionally, federated learning techniques, such as model aggregation and differential privacy, ensure that sensitive information remains protected during training. This project aims to develop more robust machine-learning models for various robotics and automation applications. By harnessing the power of federated learning, robotic systems can benefit from improved model performance, reduced communication overhead, and enhanced privacy, paving the way for more intelligent and secure automation in distributed environments.
Problem Statement
- Addressing concerns about sharing sensitive data by training models locally on edge devices.
- Minimizing communication overhead by performing model updates locally and transmitting only the necessary information.
- Accommodating diverse devices with varying capabilities and data distributions.
- Ensuring timely model updates without disrupting the operational efficiency of robotics and automation systems.
- Developing lightweight and energy-efficient algorithms to operate effectively on edge devices with limited computational resources.
Aim and Objectives
- Integrate federated learning into robotics and automation systems to improve model training efficiency while preserving data privacy.
- Develop federated learning algorithms tailored to the unique challenges of robotics and automation environments.
- Ensure real-time model updates without compromising system performance or safety.
- Address heterogeneity in device capabilities and data distributions within federated learning frameworks.
- Minimize communication overhead and bandwidth usage while maintaining model accuracy.
- Enhance security and privacy protections to safeguard sensitive data in distributed learning settings.
Contributions to Robotics and Automation using Federated Learning
- Enhanced model training efficiency optimizes model training by leveraging distributed data sources without centralization.
- Improved Data Privacy preserves data by conducting model training locally on edge devices, minimizing data sharing across networks.
- Real-time Adaptation enables rapid model updates, facilitating dynamic environments and tasks in robotics and automation.
- Support for heterogeneous devices accommodates diverse computational capabilities, allowing collaborative training across different hardware platforms.
- Resource efficiency optimizes resource usage by distributing computation and storage demands among edge devices, conserving energy and reducing server load.
- Scalability seamlessly accommodates growing numbers of edge devices and data sources, making it suitable for large-scale robotics and automation deployments.
Deep Learning Algorithms for Robotics and Automation
- Convolutional Neural Networks (CNNs)
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Recurrent Neural Networks (RNNs)
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Long Short-Term Memory (LSTM) networks
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Gated Recurrent Units (GRUs)
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Deep Reinforcement Learning (DRL)
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Transformer-based models
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Generative Adversarial Networks (GANs)
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Autoencoders
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Variational Autoencoders (VAEs)
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Siamese Networks
Datasets for Robotics and Automation using Federated Learning
- FLAIR Robotics Dataset
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RoboNet
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FERG-DB
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SURREAL
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RoboEarth
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UR5e Robot Arm Dataset
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Dactyl Dataset
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ACRV Object Recognition Dataset
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Office-31
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ARIAC Dataset
Software Tools and Technologies
Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
Development Tools: Anaconda3, Spyder 5.0, Jupyter Notebook
Language Version: Python 3.9
Python Libraries:
1.Python ML Libraries:
- Scikit-Learn
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Docker
- MLflow
2.Deep Learning Frameworks:
- Keras
- TensorFlow
- PyTorch
