Towards Embodied Intelligence: Novel Kinematic Structures and AI-Guided Mechanism Design

Dear Colleagues,

The kinematic configuration—the fundamental arrangement of joints, links, and motion primitives—forms the structural backbone of a robot, defining its workspace, dexterity, stiffness, and dynamic capabilities. As robotics progresses toward embodied intelligence, the interplay between physical morphology, control, and learning has become increasingly significant. In this context, innovative kinematic structures not only enable specialized functions but also shape how robots perceive, interact, and adapt within real-world environments.

Recent advances in parallel mechanisms, reconfigurable robots, modular manipulators, and soft robotic architectures have highlighted the essential role of structural innovation in achieving high performance across diverse tasks—from high-speed pick-and-place operations to precision-critical manipulation. Designing such configurations, however, requires addressing longstanding challenges, including singularity avoidance, workspace expansion, dynamic feasibility, and the balance between mobility and structural stability.

Emerging AI-guided approaches, such as data-driven optimization, generative mechanism synthesis, and computational design automation, are opening new possibilities for exploring vast design spaces and discovering high-performing architectures that would be difficult to obtain through traditional methods.

This Special Issue seeks to gather state-of-the-art research on kinematic design principles, mechanism innovation, and AI-enabled methodologies that advance the development of embodied and intelligent robotic systems. We invite original contributions that explore novel architectures, integrate AI into the design process, or provide new theoretical and application-driven insights into robotic kinematics. Topics of interest include, but are not limited to, the following:

• AI-guided synthesis and optimization of serial, parallel, and hybrid manipulators;
• Task-oriented, learning-oriented, and performance-based kinematic design;
• Embodied intelligence-driven morphological and kinematic co-design;
• Reconfigurable robots, modular structures, and variable topology mechanisms;
• Bio-inspired, soft robotic, and compliant mechanism architectures;
• Integration of kinematic design with control, perception, and trajectory learning;
• Generative models, computational geometry, and AI-driven mechanism synthesis;

Dr. Yang Liu
Dr. Zhiyuan Zhao
Guest Editors

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