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Improving Humanoid Safety by Combining Model-Based Control with Reinforcement Learning
We develop an algorithm to teach robots ability to balance without falling when an external push is applied. We combine model-based control inputs with model free policy learning to improve performance. We also present a curriculum to enable efficient learning.
Learning to Walk on Treadmill
We learn a control policy to create human agents that can walk on treadmill in simulation. The biomechanical gait characteristics of the human agent is similar to real-world human walking
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