Life-like robots represent a cutting-edge research area in the robotics field over the past decade. The main goal is to merge deep physical principles with biological systems at the molecular, cellular, and tissue levels, integrating them with traditional electromechanical structures. This creates a new class of robotic systems that combine the strengths of living organisms—such as high energy efficiency and intrinsic safety—with the robustness and precision of mechanical systems. These robots are expected to overcome existing limitations, including low energy conversion rates, limited compliance, and poor operational flexibility.
In recent years, most research has focused on bio-driven systems using cells and tissues, often controlled through light or electrical signals. However, due to a lack of comprehensive theoretical models for cell-based actuation, life-like robots still face significant challenges in motion control and dynamic synchronization.
Recently, a team from the Micro-Nano Robotics Group at the Shenyang Institute of Automation, Chinese Academy of Sciences, introduced a cellular mechanical dynamics model based on the subcellular structure of muscle cells. By simulating the internal components of cardiomyocytes using electromechanical elements like springs, variable dampers, and motors, they were able to capture the dynamic behavior of heart cells. Using scanning ion-conductivity microscopy, they obtained real-time pulsation data and identified key parameters of their model. This allowed them to measure multiple physical properties of single-cell substructures, such as viscosity, elasticity, mass, and action potential.
The non-invasive nature of the scanning ion-conductivity microscope enabled the accurate and lossless collection of these multi-dimensional characteristics. This breakthrough provides a solid foundation for future research on dynamic control and matching in life-like robots powered by muscle cells.
The findings were published in the *Biophysical Journal* and were highlighted as an important contribution. The study was supported by the National Natural Science Foundation of China, the Chinese Academy of Sciences, and the State Key Laboratory of Robotics.
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