Call for Paper

1. Machine Learning in Robotics

• Supervised, unsupervised, and reinforcement learning 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

• Deep learning for robot localization and mapping (SLAM)

• End-to-end learning for robotic manipulation and locomotion

• Self-supervised and semi-supervised learning for robotics

3. Machine Vision and Perception

• Image and video analysis for robotic applications

• 3D vision, structure from motion, and depth perception

• Visual tracking, object detection, and pose estimation

• Semantic and instance segmentation for scene understanding

• Vision-based manipulation, grasping, and tactile-visual integration

• Sensor fusion (vision, LiDAR, radar, IMU) and multi-modal perception

4. Robotics Fundamentals and Control

• Robot kinematics, dynamics, and motion planning

• Multi-robot systems, swarm robotics, and coordination

• Human-robot interaction and collaborative robotics

• Soft robotics, bio-inspired robotics, and reconfigurable robots

• Robot perception, state estimation, and sensor networks

• Real-time and embedded control architectures

5. Applications of Machine Learning and Vision in Robotics

• Healthcare, surgical, and rehabilitation robotics

• Industrial automation, manufacturing, and logistics

• Autonomous vehicles, drones, and intelligent transportation systems

• Service, social, and assistive robotics

• Agricultural, field, and forestry robotics

• Exploration, search and rescue, and planetary robotics

6. Theoretical Foundations and Real-World Deployments

• Advances in neural network architectures for robotic vision

• Statistical learning, scalability, and real-time efficiency

• Explainability, interpretability, and safety of robotic learning models

• Robustness, generalization, and domain adaptation in real-world environments

• Benchmarking, performance evaluation, and case studies of deployed systems

• Ethical considerations, legal frameworks, and human-centric design