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