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Found 2 papers, 1 papers with code
Tuning Legged Locomotion Controllers via Safe Bayesian Optimization
This paper presents a data-driven strategy to streamline the deployment of model-based controllers in legged robotic hardware platforms.
RL + Model-based Control: Using On-demand Optimal Control to Learn Versatile Legged Locomotion
These reference motions serve as targets for the RL policy to imitate, leading to the development of robust control policies that can be learned with reliability.
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