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RobotKeyframing: Learning Locomotion with High-Level Objectives via Mixture of Dense and Sparse Rewards
This paper presents a novel learning-based control framework that uses keyframing to incorporate high-level objectives in natural locomotion for legged robots.
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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