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
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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
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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
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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
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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
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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
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