Embodied Artificial Intelligence (Embodied AI) has emerged as a transformative paradigm that tightly couples perception, reasoning, and action in physical environments. The convergence of foundation models, world models, and advanced robotics is rapidly reshaping how intelligent agents perceive, plan, and act in the real world. This session—the 1st Special Session on Wide-spectrum Embodied Artificial Intelligence (WEAI 2026)—provides a broad, inclusive forum for researchers and practitioners across AI, robotics, software engineering, and autonomous systems to present and discuss the full spectrum of embodied intelligence. Topics span from theoretical foundations (e.g., world models, VLA models, reinforcement learning) to systems and applications (e.g., robotic manipulation, autonomous driving, digital twins, sim-to-real transfer, edge deployment, and industrial embodied systems). This session explicitly welcomes all papers related to embodied intelligence, fostering cross-disciplinary exchange between learning, control, perception, and software engineering communities.

While this session covers software engineering and artificial intelligence broadly, it lacks a dedicated venue for the rapidly expanding field of Embodied AI—a discipline that sits at the intersection of robotics, foundation models, world models, control, and software engineering for physical systems. General conference tracks cannot provide the focused, cross-disciplinary depth that embodied intelligence demands, as papers on robotic manipulation, VLA models, sim-to-real transfer, autonomous driving, and embodied software engineering are typically scattered across different sessions. This session addresses this gap by creating the first "wide-spectrum" session that explicitly welcomes any paper related to embodied intelligence, regardless of whether its primary angle is algorithmic, systems-oriented, or application-driven. This concentration of embodied-AI research within a single session will catalyze interdisciplinary dialogue, surface new research frontiers, and strengthen the connection between the software engineering and robotics communities—making it a uniquely valuable addition to the SEAI 2026 program.

 

Topics of Interest:
We welcome submissions encompassing theoretical foundations, system architectures, protocol designs, and empirical evaluations. Topics of interest include, but are not limited to:

• Embodied Learning and Decision Making (Reinforcement Learning, Imitation Learning, Offline RL, Curiosity-driven Exploration)
• World Models, Vision-Language-Action (VLA) Models, and Foundation Models for Embodied AI
• Robotic Manipulation, Locomotion, Navigation, and Human-Robot Interaction
• Sim-to-Real Transfer, Digital Twins, Physical Simulation, and Embodied AI Benchmarks
• Autonomous Driving, Multi-agent Embodied Systems, and Intelligent Vehicles
• Software Engineering for Embodied Systems, Agent Engineering, Edge Intelligence, and Industrial Applications

 

Submit Method:
1, submit it via the link: http://confsys.iconf.org/submission/seai2027 (after entering the link, click on the corresponding topic)
2, send your manuscript to seai_conf@163.com with subject "Submit+Special Session-2+Paper Title". (请通过邮件发送稿件，邮件题目：Submit+Special Session-2+Paper Title)

 

Topic chairs:

Zhenghong Yu is a Professor and Dean of the School of Robotics at Guangdong Polytechnic of Science and Technology. He received his Ph.D. in Control Science and Engineering from Huazhong University of Science and Technology in 2014. He serves as the Program Vice-Chair of SEAI 2026 and is a Guest Professor at Mahanakorn University of Technology (Thailand) and the Hubei Provincial Laboratory of Intelligent Robot. He was also a visiting scholar at Dresden University of Technology, Germany. His research focuses on Computer Vision, Intelligent Robots, and Smart Agriculture. He has published extensively in international journals and conferences, and has led multiple provincial-level research projects. He is the Deputy Secretary-General of the Robotics Committee of the Chinese Association for Artificial Intelligence, and is dedicated to bridging the gap between software engineering and physical intelligence.

 

Ning Tan is a Professor at the School of Computer Science and Engineering, Sun Yat-sen University. He received the Ph.D. degree from CNRS Femto-ST Institute / Université Marie et Louis Pasteur, France. He is an IEEE Senior Member and has been listed among the World's Top 2% Scientists. He has held research positions at the National University of Singapore (NUS) and the Singapore University of Technology and Design (SUTD). His research interests include Robotics (intelligent robots, intelligent control, medical robotics, multi-robot cooperation, bio-inspired robotics) and Artificial Intelligence (embodied AI, machine/deep learning, multimodal large models, world models, brain-inspired computing, machine vision). He has published over 100 papers in leading journals and conferences, including IEEE Transactions on Robotics (T-RO), The International Journal of Robotics Research (IJRR), Automatica, and Robotics: Science and Systems (RSS), with more than 10 authorized invention patents. He has received multiple best paper awards and serves as a core expert in the formulation of group standards for embodied intelligence.