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IREAI 2027 : 2027 International Conference on Intelligent Robot and Embodied AI | |||||||||||
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Call For Papers | |||||||||||
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2027 International Conference on Intelligent Robot and Embodied AI (IREAI 2027)
★CONTACT US Website: www.ireai.org Date: September 10–12, 2027 Venue: China Full Paper Submission: March 27, 2027 Email: contact@ireai.org Tel:+86-28-85586839 (International) Monday-Friday, 9:00am-12:00pm and 1:30pm-6:00pm ★Welcome to IREAI 2027 Welcome to the 2027 International Conference on Intelligent Robot and Embodied AI (IREAI 2027)! We are delighted to have you join us from September 10–12, 2027. As robotics and artificial intelligence continue to converge, embodied AI is redefining the way intelligent systems perceive, reason, and interact with the physical world. IREAI 2027 brings together leading researchers, engineers, and industry professionals from around the globe to explore cutting-edge advancements in robot perception and control, multi-robot collaboration, human-robot interaction, foundation models for robotics, and sim-to-real transfer for embodied intelligence. ★Publication Information Registered and presented full papers will be included in the IREAI 2027 digital conference proceedings and submitted to major citation databases (including, but not limited to Ei Compendex and Scopus) for review and indexing. ★Call for papers Topics of Interest include but not limited to: 1. Intelligent Robotics and Autonomous Systems Robot Perception, Localization, and Navigation Manipulation, Grasping, and Dexterous Control Motion Planning and Trajectory Optimization Multi-Robot Collaboration and Swarm Intelligence Human-Robot Interaction (HRI) and Collaborative Robotics Soft Robotics and Bio-Inspired Robot Design Legged, Wheeled, and Aerial Robot Systems 2. Embodied AI and Cognitive Systems Embodied Perception, Reasoning, and Decision-Making Vision-Language-Action (VLA) Models for Embodied AI Foundation Models and Large Language Models for Robotics World Models, Simulation, and Physics-Based Reasoning Sim-to-Real Transfer and Domain Adaptation Robot Skill Acquisition, Generalization, and Compositionality Affordance Learning and Object Manipulation Understanding |
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