AFTER

Call For Papers

The ICMLRSS emphasizes interdisciplinary collaboration by bringing together experts from diverse fields. It encourages research that integrates multiple perspectives to address complex global challenges.

Key areas such as Machine Learning In Robotics Software are explored to promote cross-domain knowledge exchange and collaborative innovation.

Authors are invited to submit papers addressing, but not limited to, the following areas:

Machine learning algorithms for robotics
Deep learning in robotic perception
Reinforcement learning for robot control
Data-driven robotics software development
Transfer learning in robotic applications
Machine learning for robotic manipulation
AI-driven simulation for robotics
Robustness of ML models in robotics
ML techniques for robotic vision systems
Collaborative learning in robotics software
Machine learning for autonomous navigation
Real-time learning in robotic systems
Ethics of machine learning in robotics
ML frameworks for robotic software
Scalable machine learning in robotics
Interpretable machine learning in robotics
Federated learning for robotics applications
ML for adaptive robotic systems
Benchmarking ML algorithms in robotics
Future directions in ML for robotics

Assessment

All submissions will undergo peer review to ensure quality and interdisciplinary relevance. Accepted papers will be presented and considered for publication in journals and conference proceedings.