Learning-based control is a method that uses data-driven approaches to improve the performance and adaptability of control systems in robotic applications. This approach often leverages machine learning techniques to analyze data and refine control strategies, allowing robots to adjust their behaviors based on past experiences. This adaptability is particularly useful in soft robotics, where traditional control methods may struggle to handle the complexities of soft and flexible materials.
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