Conference Information
CIR 2025: International Conference on Computational Intelligence and Robotics
https://www.ic-cir.com/
Submission Date:
2025-09-05
Notification Date:
2025-09-08
Conference Date:
2025-09-12
Location:
Guangzhou, China
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Call For Papers
CIR 2025 is a grand gathering of the world's leading scholars and industry experts, scheduled to be held majestically in the scenic city of Guangzhou, China from September 12-14, 2025. 

The conference centers on the cutting-edge fields of computing intelligence and robotics, aiming to explore the latest research outcomes, facilitate technological exchanges, strengthen international cooperation, and propel innovation and development in related domains.During the conference, attendees will delve into in-depth discussions surrounding core algorithms of computing intelligence, application cases, as well as the latest advancements in robotics technology, including but not limited to machine learning, deep learning, natural language processing, computer vision, human-robot interaction, service robots, industrial automation, among others. 

The topics of interest for submission include, but are not limited to:

*Machine Learning

· Deep Reinforcement Learning
· Unsupervised Learning
· Transfer Learning
· Meta-Learning
· Generative Adversarial Networks (GAN)

*Computer Vision
· Visual Object Tracking
· 3D Visual Reconstruction
· Scene Understanding
· Visual Navigation and Localization
· Visual Recognition in Human-Robot Interaction

*Reinforcement Learning
· Autonomous Navigation for Robots
· Multi-Agent Cooperative Reinforcement Learning
· Offline Reinforcement Learning
· Learning in Sparse Reward Environments
· Model-Based Reinforcement Learning

*Human-Robot Interaction and Collaboration
· Robot Emotion Recognition
· Verbal and Nonverbal Interaction
· Task Allocation for Collaborative Robots
· Interaction Interface Design
· Socially Adaptable Robots
· Performance Analysis and Optimization for Robots
· Sensor Data Processing and Fusion
· Online Learning and Model Updating
· Data-Driven Fault Detection and Diagnosis
· Applications of Deep Learning in Data Mining
Last updated by Dou Sun in 2025-08-02
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