ICIEA 2026 Special Session
SS15: Perception, Planning, and Control for Intelligent Robotics
Organized by:
Organizer 1: Wei Wang
Email: w.wang@buaa.edu.cn
Affiliation: Beihang University, China
Organizer 2: Jin Wang
Email: dwjcom@zju.edu.cn
Affiliation: Zhejiang University, China
Organizer 3: Silu Chen
Email: chensilu@nimte.ac.cn
Affiliation: Ningbo Institute of Materials Technology and Engineering, China
Organizer 4: Gang Chen
Email: gchen@zstu.edu.cn
Affiliation: Zhejiang Sci-Tech University, China
Organizer 5: Xiaolong Yu
Email: yxiaolong@buaa.edu.cn
Affiliation: Hangzhou Innovation Institute of Beihang University, China
The field of intelligent robotics is undergoing a profound transformation, driven by breakthroughs in control technologies and artificial intelligence. These advancements have enabled robots to move beyond structured industrial settings into complex, dynamic environments such as autonomous driving, agile logistics, human-robot collaborative manufacturing, and personalized healthcare. The integration of high-precision sensing, real-time decision-making, and adaptive control has endowed robots with unprecedented levels of autonomy and versatility. However, as robotic systems are increasingly deployed in open-world scenarios—characterized by uncertainty, incomplete information, and diverse interaction patterns—traditional approaches to robot perception, planning, and control face fundamental limitations. Conventional control theories often struggle to handle high-dimensional, nonlinear dynamics under real-time constraints, while classical planning methods may fail in environments requiring continual adaptation. Furthermore, the effective fusion of multi-modal sensory inputs into coherent world models remains a critical challenge, highlighting the need for novel methodologies that seamlessly integrate sensing, cognition, and action.
This special session, "Perception, Planning, and Control for Intelligent Robotics," focuses on the latest advancements addressing these challenges, exploring innovative perception mechanisms such as 3D scene understanding, sensor fusion, and embodied AI to enhance environmental interpretation. In planning, it seeks contributions on hierarchical task planning, motion planning under uncertainty, and human-aware planning for safe and efficient robot behavior, while in control, the session emphasizes learning-based strategies, adaptive control architectures, and hybrid methods combining model-based and data-driven techniques. The integration of AI and machine learning for real-time decision-making and system optimization constitutes a key theme, including reinforcement learning, deep perception models, and cognitive architectures. Through this session, we aim to bring together cutting-edge research on intelligent control, AI-enabled planning, multi-sensor perception, and system integration, providing insights into the future of robotic systems. We encourage submissions presenting novel methodologies, system integrations, and real-world applications that foster interdisciplinary dialogue and outline a roadmap for more autonomous, interactive, and reliable intelligent robots.
Topics of Interest (include but are not limited to):