Data-driven approaches have become increasingly relevant in robotic control as they have achieved unprecedented success in various applications, such as sliding contact control , agile and dynamic motion skills , and locomotion in the wild .
By leveraging large datasets of real-world robot motion or simulated data , data-driven approaches can learn complex control policies that effectively handle the nonlinear dynamics of the robot. Data-driven approaches can also be adaptable, enabling faster development and deployment of control policies for different tasks and environments. Therefore, data-driven approaches have the potential to improve the performance, robustness, and scalability of whole-body control for humanoid robots.
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