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Researchers have developed biohybrid rays that utilize AI-driven optimization to enhance swimming efficiency. These mini biohybrid rays, made from heart muscle cells and rubber, swim twice as efficiently as traditional models. This innovation could revolutionize biohybrid robotics, paving the way for advanced applications in medicine and environmental science.
The collaboration between Harvard SEAS and NTT Research showcases the potential of machine learning in designing bioinspired robotics. The research highlights the effectiveness of machine-learning directed optimization in achieving superior performance in biohybrid designs. This breakthrough not only demonstrates the capabilities of AI in engineering but also opens new avenues for future research and applications.
• AI-driven optimization enhances swimming efficiency in biohybrid robotics.
• Machine learning enables superior design configurations for biohybrid rays.
Machine learning is a subset of AI that enables systems to learn from data and improve over time, as demonstrated in the design of biohybrid rays.
Biohybrid robotics combines biological materials with synthetic components, exemplified by the mini biohybrid rays created in this research.
Optimization in this context refers to the process of improving design configurations using machine learning to achieve better performance in swimming efficiency.
Harvard SEAS focuses on engineering and applied sciences, contributing to advancements in biohybrid robotics through innovative research.
NTT Research specializes in medical and health informatics, collaborating on projects that leverage AI for enhanced robotic designs.
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