Topics of ARAEML 2026 征稿主题

The 2026 IEEE 3rd International Conference on Advanced Robotics, Automation Engineering and Machine Learning (ARAEML 2026) invites submissions of original research papers, review articles, and technical case studies. Topics of interest include, but are not limited to, the following:

 

Track 1: Robotics and Control

• Robot Design, Development, and Control

• Mobile Robotics and Autonomous Navigation

• Tele-robotics and Remote Operation

• Space, Underwater, and Field Robotics

• Search, Rescue, and Disaster Response Robotics

• Micro Robots and Micro-manipulation

• Soft Robotics and Biomimetic Systems

• Multi-robot Systems and Swarm Robotics

• Robot Kinematics, Dynamics, and Motion Planning

 

Track 2: Robotics Perception and Intelligence

• Robot Perception, Sensing, and Awareness

• Multi-sensor Fusion and Data Integration

• Visual Computing and Intelligent Robotics

• 3D Vision, Depth Estimation, and Scene Understanding

• Simultaneous Localization and Mapping (SLAM)

• Object Detection, Recognition, and Tracking in Robotics

• Cognitive Approaches for Robotics and Mobile Robots

• Affective and Social Robotics

 

Track 3: Automation and Intelligent Systems

• Automation Engineering and Industrial Systems

• Intelligent Manufacturing, Industry 4.0, and Smart Factories

• Process Control, Monitoring, and Optimization

• Industrial Internet of Things (IIoT) and Cyber-Physical Systems

• System Integration, Automation Architecture, and Interoperability

• Predictive Maintenance and Fault Diagnosis

• Flexible Manufacturing and Collaborative Robotics

• Mobile Sensor Networks and Distributed Automation

 

Track 4: Machine Learning Theory and Algorithms

• Supervised, Unsupervised, and Semi-supervised Learning

• Reinforcement Learning and Deep Reinforcement Learning

• Deep Learning Architectures (CNN, RNN, Transformer, GNN, etc.)

• Generative Models (GANs, VAEs, Diffusion Models)

• Transfer Learning, Meta-Learning, and Few-shot Learning

• Federated Learning and Privacy-Preserving Machine Learning

• Explainable AI (XAI) and Interpretable Machine Learning

• Continual and Lifelong Learning

• Probabilistic Models and Bayesian Inference

• Optimization Methods and Neural Architecture Search

• Self-supervised and Representation Learning

• Adversarial Learning and Robustness

 

Track 5: Machine Learning Applications in Robotics and Automation

• AI-Driven Robotics and Autonomous Systems

• Learning for Manipulation, Grasping, and Dexterous Control

• Imitation Learning and Learning from Demonstration

• Human-Robot Interaction and Collaboration

• End-to-End Learning for Robot Control

• Vision-Language Models and Multimodal Learning for Robotics

• Machine Learning for Autonomous Driving and Intelligent Vehicles

• AI-Based Motion Planning and Decision Making

• Learning-Based Perception and Scene Parsing

• Robot Learning in Unstructured and Dynamic Environments

 

Track 6: Machine Learning for Data Science and Engineering

• Predictive Analytics and Anomaly Detection

• Time-Series Analysis and Forecasting

• Computer Vision and Image Processing

• Natural Language Processing (NLP) and Understanding

• Graph Neural Networks and Relational Learning

• Data Mining, Knowledge Discovery, and Big Data Analytics

• Machine Learning for Healthcare, Finance, and Smart Cities

• AI for Scientific Discovery and Engineering Design

• Edge AI and On-Device Machine Learning