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Learning Smooth Humanoid Robot Locomotion Using Lipschitz-Constrained Policies for Robust Real-World Transfer


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Lipschitz-Constrained Policies (LCP), a novel method using a differentiable gradient penalty to enforce smooth action outputs, offers a simple and effective alternative to traditional smoothing techniques for training robust locomotion controllers in humanoid robots, enabling successful sim-to-real transfer.
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Chen, Z., He, X., Wang, Y.-J., Liao, Q., Ze, Y., Li, Z., Sastry, S. S., Wu, J., Sreenath, K., Gupta, S., & Peng, X. B. (2024). Learning Smooth Humanoid Locomotion through Lipschitz-Constrained Policies. arXiv preprint arXiv:2410.11825.
This research paper aims to address the challenge of transferring reinforcement learning (RL) based locomotion policies from simulation to real-world humanoid robots by introducing a novel method called Lipschitz-Constrained Policies (LCP) for enforcing smooth and robust behaviors.

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