The main focus of the conference is innovative and original research results in the areas of theoretical findings, design, implementation, and applications. Both theoretical paper and simulation (experimental) results are welcome.

Topics of interests include, but are not limited to the below:

1. Machine Learning in Robotics

    Supervised, unsupervised, and reinforcement learning techniques for robotics
    Adaptive and autonomous robot control systems
    Robot learning from demonstration and imitation
    Learning-based motion planning and navigation
    Multi-robot learning and coordination
    Transfer learning and domain adaptation for robotic applications

2. Deep Learning in Robotics

    Deep reinforcement learning for robotic control
    Convolutional Neural Networks (CNNs) for robotic perception
    Recurrent Neural Networks (RNNs) for sequential robot tasks
    Generative Adversarial Networks (GANs) for robotic creativity and problem-solving
    Deep learning for robot localization and mapping (SLAM)
    End-to-end learning for robotics

3. Machine Vision and Perception

    Image and video analysis for robotic applications
    3D vision and depth perception in robotics
    Visual tracking and object detection
    Semantic segmentation for robot understanding
    Vision-based manipulation and grasping
    Sensor fusion and multi-modal perception

4. Applications of Machine Learning and Deep Learning in Robotics

    Healthcare and medical robotics
    Industrial automation and manufacturing
    Autonomous vehicles and drones
    Service and assistive robots
    Agricultural robotics
    Exploration and planetary robotics

5. Theoretical Foundations and Methodologie

    Advances in neural network architectures for robotics
    Statistical learning methods in robotic applications
    Scalability and efficiency of learning algorithms for real-time robotics
    Explainability and interpretability of machine learning models in robotics
    Safety, robustness, and ethical considerations in robot learning

6. Case Studies and Real-World Deployments

    Case studies demonstrating successful implementation of machine learning and deep learning in robotics
    Challenges and lessons learned from real-world deployments
    Performance evaluations and benchmarking of machine learning models in robotics
    Interdisciplinary approaches combining robotics, machine vision, and learning algorithms